1

BKM07GM Research Methods

Rotterdam School of Management, Erasmus University

December 2013

Group 04

Lianka Bruijnen 401057

Pauline Joris 401264

Péter Káplár 402848

Pedro Luis Barrera Albarello 400503

Homeowners’ Willingness to Switch from Fixed Energy Tariffs to Real-Time Tariffs in the Netherlands

Abstract

Real time energy tariffs, which take into consideration the fluctuating costs and energy supply that occur throughout the day and adjust the tariff accordingly, are attributed to have several benefits, including the incentive to shift usage to off-peak times, thus leveling off demand and increasing system capacity, which leads to increased grid stability. This report aims to define the factors that influence people’s willingness to switch to real time tariffs. It studies the relation between attitude towards renewable energy, flexibility and subjective norms, and the willingness to switch to real time tariffs, while taking into account the place of birth and place of residence. Several interviews were done in a qualitative research and a survey was conducted among homeowners (N=43) in the Netherlands. A model was generated based upon a literature review and the interviews. A quantitative analysis was set up to test these hypotheses. The findings confirm a relation between flexibility and willingness to switch to real time tariffs, and between subjective norms and the willingness to switch to real time tariffs, and the latter relationship is influenced by other variables. Attitude towards renewable energy was not found to be related to willingness to switch in the total sample, however, it seems to be moderated by respondents’ place of residence.

Keywords: real time tariffs, renewable energy, willingness to switch, attitude, flexibility, subjective norms, Netherlands

2

INTRODUCTION

Widespread is the view amongst

economists that a number of problems of

economic nature, such as reduced

efficiency, undue cross-subsidization, and

the bargaining power of wholesale energy

suppliers stem in the energy industry from

flat pricing, which is the way the majority of

retail energy consumers pay their bills for

energy usage (Borenstein, 2012; Popov,

2012). Primarily, flat tariffs cannot meet

two out of the four general goals of any

tariff design (revenue adequacy, efficiency,

stability, and avoiding undue cross-

subsidization), because consumers are

shielded from the fluctuations in wholesale

prices, and such insulation results in prices

that can diverge considerably from

efficient levels and leads to substantial

cross subsidies (Borenstein, 2012; Popov,

2012).1,2 Further, time-insensitive flat

tariffs do not create incentives for retail

users to respond to short term energy

imbalances, but, on the other hand,

suppliers must meet the demand needs at

all costs, which gives remarkable

bargaining power to wholesale energy

suppliers (Borenstein, 2012; Popov, 2012).

The growing use of renewable energy puts

another kind of pressure on energy

providers. As Popov (2012) explains, the

1 As Borenstein (2012:7) explains, “flat rate tariffs

result in a form of inter-household group pooling of

revenue responsibility. Just as with insurance,

however, all customers do not necessarily impose the

same average costs on the system. If rates do not

recognize these differences it results in cross-

subsidies and inefficient incentives.” 2 Cross-subsidies exist between those who consume

at peak, or in high-cost locations, or in a period in

output of renewable energy generators is

intermittent and highly dependent on

weather conditions. As it is very costly to

store energy, a large share of renewable

energy sources, without the adjustment of

energy demand, may endanger grid

stability and jeopardize the smooth supply,

making it a very difficult and expensive

challenge for the supply side to meet real

time demand.

To solve these problems, it seems

inevitable to involve the demand side of

the market (Popov, 2012). This can be

possible by introducing different types of

energy tariffs3 (Verhagen, 2012), of which

we now focus on real time tariffs. While flat

rate tariff can be conceived as consumers

paying a risk premium to be protected

against the real fluctuating cost of

consuming electricity, in the case of real

time tariffs this risk premium is eliminated

and consumers are exposed to price

fluctuation. Real time tariffs are attributed

to have several benefits, including the

incentive to shift usage to off-peak times,

thus leveling off demand and increasing

system capacity, which leads to increased

grid stability and lowers the need for

investment in reserve generation capacity.

Further, they offer the possibility to save

which retail prices are not adequate to cover

wholesale costs, versus those who consume off-

peak, or in low-cost locations, or in a period in which

retail prices are set above costs to make up for

previous shortfalls. (Borenstein, 2012) 3 E.g. time of use rate, critical peak pricing, real time

pricing day ahead rate, real time pricing real time

rate, etc.

3

on electricity consumption4. (Borenstein,

2012; Popov, 2012) However, drawbacks of

real-time tariffs – the risk that the

electricity bill may fluctuate more, and that

more cognitive efforts are needed to

handle the changing prices – make it

particularly challenging for energy

providers to promote it (Popov, 2012,

Verhagen, 2012) The focus of attention in

this report therefore is on what factors

drive homeowners’ willingness to switch

from fixed energy tariffs to real time tariffs.

PROPOSED MODEL

Research background Previous research on

the topic of willingness to switch to real

time tariffs such as Popov (2012) examined

the influence of regulatory focus, need for

cognition, risk taking attitude, and attitude

towards renewable energy. Popov (2012)

found that people with a positive attitude

towards renewable energy have a

significantly higher

preference for the

choice of green energy

tariffs. Ajzen’s theory of

planned behaviour

(2006) on the other hand

showed perceived

control and subjective

norms are also

influencing willingness

to switch from flat tariffs

to real time tariffs. Both

conclusions have been

4 Other benefits include the integration of

intermittent energy generation resources, such as

wind or solar power, and the potential improvement

taken into account while generating the

model.

Interviews The interviews conducted in the

qualitative part of this research have been

partly in line with previously stated

variables and have pointed out other

examples of influential factors that lead to

people’s willingness to switch to real time

tariffs. For example in the interviews the

notion that people adjust their behaviour

to the attitude within social groups that

people relate to, such as family and friends

was backed up. This is referred to as

subjective norms, as found by Ajzen.

Furthermore it was perceived that in the

case of real time tariffs, flexibility in daily

routines could affect people’s willingness

to switch to real time tariffs since they are

time constrained. From the interviews it

became clear that moderators such as

place of residence, which would be either

in the city or the countryside, can also be of

in the cost-effectiveness of electric vehicles.

(Borenstein, 2012) Figure 1 : Model

4

influence on the attitude and flexibility This

is mainly because governmental

regulations on for example separating

trash can differ and are less strict in cities

than in the countryside. Place of birth on

the other hand influences subjective norms

as it is culturally related.

Proposed Model and Hypotheses The

model takes into account the most

important variables that impact one’s

willingness to switch to real time tariffs.

The model consists of 3 explanatory

variables, 2 interacting variables and the

dependent variable: willingness to switch

from fixed tariffs to real time tariffs. The

explanatory variables are: Attitude towards

renewable energy, Subjective norms and

Flexibility. In the model (Figure 1), all

explanatory variables are assumed to

positively influence the dependent

variable.

The first variable taken into account is the

attitude towards renewable energy. Based

on Popov (2012), who found that a positive

attitude towards the environment

positively influenced the willingness to

switch to real time tariffs, in the model it is

assumed that:

H1: A positive attitude towards renewable

energy will increase the willingness to

switch to real time tariffs.

The nature of real time tariffs requires that

flexibility is included in the model. Fixed

tariffs do not influence the time of use of

electricity, but as soon as the price of

energy consumption is time dependent,

people will be required to take more

consideration in their scheduling of

different activities. The interviews

suggested that limited flexibility can

decrease people’s willingness to switch to

real time tariffs because it is not possible

for them to change daily routines to

cheaper time periods and thus their

electricity bill would increase.

H2: Flexibility in daily routines is positively

related to the willingness to switch to real

time tariffs.

The third relation in the model is based on

Ajzen’s theory of planned behaviour which

indicates that people are likely to adjust

their behaviour to the behaviour of those

they feel closely affiliated to, such as

friends or family. So if they feel a social

pressure to switch to real time tariffs they

will be more likely to do so.

H3: The perceived social pressure to use

renewable energy will increase the

willingness to switch to real time tariffs.

Apart from the variables directly related to

the dependent, two more moderating

variable were taken into consideration,

namely the place of birth and place of

residence. Place of residence moderates

the relation between attitude towards the

renewable energy and the willingness to

switch to real time tariffs, because city

residences do not have the time or take the

effort to actually do something to benefit

the environment. The place of birth on the

other hand interacts with the relation

between social norms and the dependent,

since it can differ among cultures.

H4: The positive influence of attitude

towards renewable energy on willingness

to switch to real time tariffs is expected to

be larger for those living in the countryside

than for those living in the city.

5

H5: The positive influence of subjective

norms on the willingness to switch to real

time tariffs is expected to be related to

place of birth.

Place of residence may also be in a direct

causal relation to flexibility, it is assumed

that people who live in cities are often less

flexible because of their more hectic life

and 9 to 5 work jobs. Therefore the last

hypothesis is:

H6: The flexibility in daily routines is higher

for those living in the countryside than

those living in the city.

METHOD

Interviews and online survey

As explained in the model, this study aims

at evaluating the willingness of

homeowners in the Netherlands to switch

from fixed tariffs to real-time tariffs. In

order to gather enough information and

data for the subsequent analysis, both

interviews and an online survey were

carried out. The survey was carried out in

the Netherlands and accordingly, different

cities were surveyed, with predominance

for Amsterdam and Leiden. The surveying

took part in a two-phase process: first the

interviews and then the online survey. Two

persons were interviewed in order to

conduct a pilot. After the analysis of the

transcriptions, some questions were

adapted or removed. It appeared that

some questions, although interesting and

relevant for our survey, were not well

perceived by the respondents. Four more

interviews were then carried out to

evaluate the adapted questionnaire and to

have a first insight of what factors are

important for the willingness to switch to

real-time tariffs. In light of these results,

the proposed model was refined, the

hypotheses were adapted, and the online

survey was made. Survey items were

generated to reflect variables in our

conceptual model: Attitude towards

renewable energy (ATRE), Subjective

norms and Flexibility. As depicted in Table

1, each theme was associated with various

categories.

The questionnaire, given in Appendix I,

starts with a short introduction explaining

the goal and the survey to the

interviewees, and includes a short

explanation about real-time tariffs. The

first questions were demographic

questions including age, gender, place of

birth, the place where respondents have

been raised up, place of residence, and

level of education. Demographic variables

were expected to have no direct

relationship with willingness to switch to

real time tariffs, but to moderate the effect

of other variables. The last part of the

questionnaire consists of six-point Likert

scale questions. Scale items were

Themes Categories

SES - Age education, cultural background - Living circumstance - Willingness to switch depending on profitability

Attitude towards renewable energy

- Green awareness - Willingness to change for the environment

Subjective norms - Influence of friends and family’s attitude - Influence of society norms - Influence of education

Flexibility - Daily routines - Willingness to adapt these routines

Table 1 : Interview scales

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generated for assessing respondents’

flexibility in scheduling their daily routines.

The scale measuring respondents’ attitude

toward renewable energy was retrieved

from Popov (2012) and the scale assessing

the effect of perceived subjective norms

has been made by adapting items from the

Consumer health informatics research

resource and from Ajzen (2010). The

questions were mixed together.

The scale analysis and the analysis of the

results was carried out with SPSS version

20.0.

Data analysis

During scale analysis, each respondent’s

mean on the items within that scale was

computed for each scale, after reflecting

reverse items. Scales ranged from 1

(strongly agree) to 6 (strongly disagree). On

the attitude scale, high scores indicate

strong support for the use of renewable

energy. On the flexibility scale, high scores

represent a high ability of respondents to

have flexible daily schedule. On the

subjective norms scale, high scores

represent high perceived social pressure to

use renewable energy. Scales’ internal

consistency was tested by calculating

scales’ Cronbach’s alpha. Items that

lowered scales’ internal consistency were

excluded. The resulting Cronbach alpha

coefficients can be seen in table 2.

The analysis of the flexibility scale led to the

removal of the item 7 (“I am never at home

on weekdays between 9am to 5pm”), the

analysis of the Social Norm and of the

attitude led respectively to the removal of

the items 19 (“Most people like me would

switch from flat energy tariffs to real time

tariffs”) and 26 (“I separate my trash”) –

leading to increases in the corresponding

Cronbach’s alpha and thus in the reliability

of the survey.

Scale Cronbach’s alpha

Flexibility 0.568

Social Norms 0.615

Attitude 0.792

Survey 0.626

Table 2 : Cronbach’s

alpha

Sample

The target group is homeowners currently

living in the Netherlands, which were

considered to be the Dutch population

aged above 20 years. The numbers used

were retrieved from the Centraal Bureau

Statistiek (CBS) and are for 2009. The

population of the Netherlands is 16 485

787 persons among which 76.1% are older

than 20 years. The variance of the

population is difficult to assess, the formula

used is retrieved from the works of Air

University (2002) and is as follows:

Where n is the minimal sample size

required, N is the studied population, Z is

the corresponding value of the standard

distribution and d is the precision level.

Computing the formula with the correct

values, and for a precision level of 5% gives

n= 399 which is higher than the number of

responses received. Given the 43 answers

7

that were considered in this study, the

sample studied is not at all representative

of the Dutch adult population. As a

consequence, no conclusion drawn for the

sample can be extended to the population.

Given the context of this survey,

conducting a random survey was

complicated, thus the snowball sampling

method was used. The snowball sampling

consists of the utilization of social networks

that exist between members of a target

population to build a sample (UCGS, 2013).

The process is as follows: the survey is sent

to a first sample called the “seed” which

will in turn contact other respondents,

creating several waves of answers which

can be seen on Fig.1 illustrating the

response rate to the online survey.

Because of this technique, however some

sample bias can be observed. Indeed, all

the respondents have a good level of

education.

Univariate Outcomes

The sample size was N = 43 and the results

from the demographic variables such as

gender, age and place of birth can be seen

in Table 3. From the 43 persons who

completed the survey, 28 where female

and 15 were male. It can be seen that a

large majority of the respondents were

female as a result of one of the bias of the

sampling method. Indeed, the survey was

sent to a group of entrepreneurial women.

Due to the two extremely different

populations utilized as “seed”, the age of

the respondents is not limited to one age

group. The mean age of our sample is 37

years (SE = 2.365). The age group of 20 to

29 years is more represented. This is a

result of the sampling method used, as one

of the seed group was made of

international students.

Frequency Percent

Gender Female 28 65.1

Male 15 34.9

Age [20;30[ 24 55.9

[30;40[ 3 6.9

[40;50[ 4 9.3

[50;60[ 7 16.3

[60;70[ 5 11.6

Place

of birth

Outside

NL

12 27.9

Inside

NL

31 72.1

Sample 43 100

Table 3: Control variables

The distribution obtained for the age of the

participants can be seen on Fig.3. Visually,

it can already be seen that there is few

chances that the distribution will be

normal. A Shapiro-Wilk’s test (p <0.05) and

a visual inspection of the Q-Q plots and the

box plots showed that the age was not

normally distributed with a skewness of

0.691 (SE = 0.361) and a kurtosis of -1.013

Figure 2: Daily responses to the

survey

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(SE = 0.709) the p-value obtained was p=

0.000 which led to the rejection of the null

hypothesis stating that the data are

normally distributed.

.

The analysis of the characteristics of the

dependent variables can be seen in Table 4

and Fig.2. It is important to remember that

the sample used was not representative

from the population and that consequently

no conclusion can be generalized from the

sample to the population. As seen on Fig.2,

the several dependent variables seem to

follow a unimodal distribution.

Normality.

For each variable, normality was tested by

the Shapiro-Wilk test. For this test, the null

hypothesis is that the observed distribution

fits the normal distribution. The test results

can be found in Table 5.

Normality Test

Shapiro-Wilk

Statistic df Sig.

Flexibility 0,959 43 0,129

subjective

norm 0,976 43 0,485

Attitude 0,939 43 0,024

Table 5: Normality test

As normality is assumed, a p-value below

the 0,05 indicates that the null hypothesis

can be rejected as the probability of it

being true is lower than the chosen

significance level. Based on test results,

flexibility and subjective norm are normally

distributed (W(43)=0,959, p=0,129, and

W(43)=0,976, p=0,485), while the

probability of the distribution of attitude

fitting normal distribution was below the

acceptable level (p=0,024).

Attitude Subjective

Norm

Flexibility

Mean 4.403 3.192 4.465

Median 4.500 3.250 4.400

Mode 4.170 3.500 4.000

Std

Deviation

0.812 0.874 0.709

Variance 0.659 0.764 0.502

Table 4: Scales characteristics

Figure 3: Histogram of the age of the

respondents.

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Figure 4: Histogram of the scales [mean items]

10

Bivariate outcomes

Correlations. The assumed relationship

between attitude towards the use of

renewable energy, flexibility, subjective

norms, and willingness to switch, were

assessed by a correlation analysis. As the

calculation of the Pearson correlation

coefficient requires the joint normality of

the associated variables, and the attitude

explanatory variable departed from

normality according to the Shapiro-Wilk

test, Spearman’s rank correlation

coefficient was used to assess the assumed

relationship between respondents’

willingness to switch and each explanatory

variable. The null hypotheses for H1, H2

and H3 was that there is no linear

relationship between the variables, that is,

H: ρ=0, while the alternative hypotheses

are: H1: ρ>0, H2: ρ>0 H3: ρ>0. The

correlation matrix can be found in Table 5.

A middle level, significant correlation has

been found between flexibility and

willingness to switch (ρ= 0,424, p= 0,005).

R2, the shared variance in the two ranked

variables, amounted to 18%. Similarly, a

low level, significant correlation has been

found between subjective norm and

willingness to switch (ρ= 0,311, p= 0,042),

with a shared variance of 10%.

Inconsistent with what was expected, no

correlation has been detected between the

ranked variables attitude and willingness to

switch (p= 0,835).

Based on the correlation analysis of the

results, there is sufficient empirical

evidence against H10 and H20, that is, the

assumption that flexibility and willingness

to switch, and subjective norms and

willingness to switch are not related, can

be rejected.

t-test for testing differences in willingness

to switch between subsamples.

Differences in willingness to switch to real

time tariffs between subsamples were

measured with independent samples t-

test. The null hypothesis for each test was

that no difference exists between mean

willingness to switch in subsamples, as

operationalized by the mean of their

responses on a six point Likert scale (H0:

µvillage – µcity = 0, with the corresponding

hypotheses: µvillage – µcity ≠ 0 etc.)

Consistent with what was expected, t-tests

revealed no significant difference in

willingness to switch to real time tariffs in

Willingness to

switch

Flexibility Subjective

norm

Attitude

Willingness to

switch 1,000 ,424** ,311* ,033

Flexibility * 1,000 ,229 ,106

Subjective norm 1,000 ,185

Attitude 1,000

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table 6: Spearman correlation coefficients

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subsamples based on demographic

variables: gender (t(41)=-0,441, p=0,661)

place of birth (t(41)=-0,077, p=0,939), and

place of residence (t(41)=-0,178, p=0,860).

It was further hypothesized that there is a

significant difference in respondents’

flexibility based on place of residence. This

was tested with an independent sample t-

test, with the null hypothesis being that

there is no difference in the flexibility of

respondents in the two subsamples, as

operationalized by the mean (H0: µvillage –

µcity=0). Inconsistent with what had been

expected, the null hypothesis cannot be

rejected below the required 5% probability

of error based on the test result, thus H6

has been rejected.

Correlation within subsamples. The same

correlation analyses as outlined above

were used to explore the relationships of

attitude, and subjective norms to

willingness to switch to real-time tariffs

within subsamples of place of residence

and place of birth.

Within the two subsamples of place of

residence, salient differences have been

found in the relationship between attitude

and willingness to pay: while no correlation

has been detected between attitude and

willingness to switch (p= 0,113) in the city

subsample, a very strong and significant

correlation has been found between

attitude and intention to switch (ρ=0,838,

p<0,001) in the village subsample, with

70% shared variance in the ranked

variables. This suggests that the

relationship between attitude and

willingness to switch may not be linear but

is moderated by respondents’ place of

residence. This implies that, consistent

with H4, an increase in attitude towards

the use of renewable energy is only

accompanied by an increase in willingness

to switch in the part of the population that

lives in villages.

Within the two subsamples of place of

birth, the correlation analysis also revealed

differences in the relationship between

subjective norm and willingness to switch.

While a middle level, significant correlation

has been found between subjective norm

and willingness to switch in respondents

who were born in the Netherlands (ρ=0,39,

p=0,044), with 16% shared variance the

ranked variables, no significant correlation

has been detected in the subsample

consisting of respondents born outside of

the Netherlands (p=0,463). This suggests

that the null hypothesis of H5 holds can be

rejected, although the opposite direction

was hypothesized.

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REGRESSION ANALYSIS

In order to evaluate the effect that the

independent variables have on the

dependent variables, two models were

used and three control dummy variables

were included: gender (dFemale), place of

birth (dPlaceofbirth) and in which location

the people live (dlocationlive).

As it can be seen in table 8, in the first

model all the variables were included:

attitude (mean_attitude), flexibility

(mean_flexibility), subjective norms

(mean_subjective_norm) and the three

control variables just mentioned. Since the

significance (Sig. F Change) is 11,5%, which

is above level of significance of 5%, the null

hypothesis cannot be rejected (H0: βj=0),

which means that the predictors could

have no effect in the model. Thus asecond

model was ran in which the non control

variables were excluded obtaining a

significance of 2,2% which is below the

level of significance of 5%. There is thus

sufficient statistical evidence to reject the

null hypothesis (H0), proving that changes

in the model affect the dependent variable.

Regression Results : Willingness to change

Model I Model II

Intercept 0.747

(1.759)

4.627

(0.533)

dFemale -0.254

(0.374)

-0.185

(0.407)

dPlaceofbirth 0.128

(0.384)

0.046

(0.419)

dLocationlive 0.185

(0.421)

-0.067

(0.437)

mean_flexibility 0.621

(0.263)

mean_subjective_

norm

0.364

(0.208)

-

mean_attitude -0.053

(0.226)

-

R² 1.135 -

Rmse 43 -

N 0.006

Table 8: Regression Results.

This is to say that the non-control variables

are a meaningful addition to our model, as

changes in the predictor’s value are related

to changes in the response variable.

Model R R Square Adjusted R

Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change F Change df1 df2

Sig. F

Change

1 ,486a ,237 ,109 1,135 ,237 1,859 6 36 ,115

2 ,078b ,006 -,070 1,244 -,231 3,623 3 36 ,022

a. Predictors: (Constant), mean_attitude, dFemale, mean_flexibility, dPlaceofbirth,

mean_subjetive_norm, dLocationlive

b. Predictors: (Constant), dFemale, dPlaceofbirth, dLocationlive

Table 7. Model Summary

13

Therefore the non control variables cannot

be excluded of the model.

In Table 8 the results of the regression

analysis model are shown, the numbers in

parenthesis under the coefficients are the

standard errors related to the coefficients

(numbers that are not in parenthesis), n

represents the number of observations,

Rmse the standard error of the regression

and finally R2 represents the coefficient of

correlation, which according to Newbold et

al. (2010) can be interpreted as the

percentage of variability in the dependant

variable (willingness) that is explained by

the regression equation, vary from 0 to 1,

in this case is equal to 0,237, this is to say

that the model has a moderate explanatory

power.

The results indicate that the conditional

estimates of the effects of the predictor

variables (excluding the control variables)

are as follows:

1. An increase of one unit in the mean of

the flexibility leads to an expected

increase in the willingness by 0,621

units.

2. An increase of one unit in the mean of

the subjective norms leads to an

expected increase in the willingness by

0,364 units.

3. An increase of one unit in the mean of

the attitude leads to an expected

decrease in the willingness by 0,053

units.

It is necessary to emphasize that these

coefficient estimates are valid only for a

model with all previous predictor variables

included.

Before analyzing the model, it is necessary

to make sure that there is no

multicollinearity between the independent

variables. Since all the correlations of the

variables are below 0.425 there is enough

statistical information to conclude that

there is no multicollinearity.

To better understand the accuracy of these

effects two things can be done: construct

conditional 95% confidence intervals or

calculate the p-value of each variable and

compare them to level of significance, in

this case 5%. For the purposes of this report

both things were done using SPSS, the

results are shown in Table 8.

The null hypothesis (H0: βj=0) can be

rejected if the p-value of the variables is

below the level of significance (p-

value<0.05), or if the confidence interval

contains the number zero. If the null

hypothesis cannot be rejected that means

that the β is not different from zero.

In this case the results for the p-value (Sig.)

are consistent with the results of

the confidence interval, this is to say that

they are the same for each variable, which

means that the method chosen is

indifferent to the results.

Only in the case of the variable flexibility,

denoted as mean_flexibility, there is

sufficient statistical information to reject

the null hypothesis, since the interval does

not include the number zero, thus it can be

said that is statistically significant. On the

other hand in the case of the control

variables, the attitude and the subjective

norms there is not enough statistical

14

information to reject the null hypothesis,

therefore can be concluded that they are

not statically significant. Again, this is only

true only for a model that includes all the

predictor variables mentioned above.

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig. 95,0%

Confidence

Interval for B

B Std.

Error

Beta Lower

Bound

Upper

Bound

1

(Constant) ,747 1,759

,425 ,674 -2,820 4,313

dFemale -,254 ,374 -,102 -,678 ,502 -1,012 ,505

dPlaceofbirth ,128 ,384 ,053 ,334 ,740 -,650 ,906

dLocationlive ,185 ,421 ,071 ,439 ,663 -,668 1,038

mean_flexibility ,621 ,263 ,366 2,361 ,024 ,088 1,155

mean_subjetive_norm ,364 ,208 ,265 1,746 ,089 -,059 ,787

mean_attitude -,053 ,226 -,036 -,236 ,815 -,512 ,406

2

(Constant) 4,627 ,533

8,679 ,000 3,549 5,706

dFemale -,185 ,407 -,074 -,454 ,652 -1,008 ,638

dPlaceofbirth ,046 ,419 ,019 ,110 ,913 -,801 ,893

dLocationlive -,067 ,437 -,026 -,153 ,879 -,950 ,817

a. Dependent Variable: 28.If I was given the opportunity, I would intend to switch

from flat energy tariffs to real time tariffs.

Table 9. Coefficientsa

15

CONCLUSION

This report researched respondents’

willingness to switch to real time tariffs

motivated by its importance for the use of

renewable energy. It aimed to determine

which variables have an influence on

people’s willingness to switch tariffs by

taking into account respondents’ attitude

toward renewable energy, flexibility in

their daily routines and social pressure they

perceive to use renewable energy.

Moderating factors such as the type of

respondents’ place of residence and their

cultural background were also taken into

account. Both qualitative and quantitative

analyses have been performed. A literary

review of existing research had been done

as well as interviews with several home

owners to define the model. A survey was

then carried out among homeowners in the

Netherlands with a total number of

respondents of N= 43. A quantitative

analysis of the results was then performed

to test the hypotheses.

In the present report, H1 and H6 have been

rejected based on the bivariate tests and

H2, H3 and H4 have been confirmed.

Although the null hypothesis of H5 has

been rejected, the opposite direction was

hypothesized than what the data

suggested. This means that in the sample

studied, attitude towards renewable

energy did not influence respondents’

willingness to switch to real time tariffs, but

flexibility and subjective norms were

moderately related to it did. No significant

difference has been found in the level of

flexibility related to the place of residence,

however, place of residence does

moderate the relation between attitude

towards renewable energy and willingness

to switch to real time tariffs. Place of birth,

or cultural differences, have been found to

have an influence on the relation between

subjective norms and the dependent.

People that were born in the Netherlands

show a higher probability to switch

because of subjective norms than those

from outside the Netherlands. This is

against previous assumptions that were

made thinking Dutch people would be

more individualistic and be less influenced

by subjective norms. The regression

analysis has shown that subjective norms

are moderated by the other variables, and

that flexibility has a significant effect on

willingness to switch, but the regression

analysis did not confirm the other

hypotheses, since there was not enough

statistical information.

A potential explanation for the attitude not

having been significantly related to

intention to switch is social conformity:

respondents tended to answer more

positively to questions regarding their

attitude to renewable energy, the

distribution of the answers is skewed to the

positive side and does not fit normal

distribution.

The fact that there were not many

significant outcomes in the regression

analysis can be due to several limiting

factors, especially in sampling, that were

present in this research. First of all the

sample size of 43 respondents is very small

to be representative of the population and

this limits the validity of statements

inferred from this sample. Secondly the

sample used was not representative of the

whole population considering the use of

16

the non-probability snowball method

which limits respondents to the same social

groups, which is reflected in highly

educated people and women being

overrepresented in the sample.

In the interviews some other factors

where spotted but not taken into account

in this report. One thing that came out was

that money is an issue when deciding to

switch or not. When consumers perceive

their bill to increase because of the switch

to real time tariffs they would be less

willing to switch than when they were

presented with options to lower their

energy bills. Secondly the government can

have great influence on people’s attitudes

towards the environment by making them

more aware, for example by implementing

regulations such as that for trash

separation. Through subsidies they can

incentivize those who fear an increase in

their energy bill and reward those who are

acting environmentally conscious.

In conclusion, in this research only limited

statistical information was available to get

a clear view of the hypotheses. The only

variable that was found to be significantly

effecting willingness to switch to real time

tariffs was respondents’ flexibility. A

broader and more elaborative research on

the topic could increase the validity of the

output.

REFERENCES

Air University (2002). Sampling and Surveying Handbook. Retrieved from: http://www.au.af.mil/au/awc/awcgate/edref/smpl-srv.pdf

Ajzen, I. (2006), Constructing a theory of planned behavior questionnaire, TPB Questionnaire Construction. Taken from: http://people.umass.edu/~aizen/pdf/tpb.measurement.pdf, 12-12-2013.

Borenstein, S. (2013, Published online: 2012) Effective and Equitable Adoption of Opt-In Residential Dynamic Electricity Pricing. Review of Industrial Organization, 42(2), 127-160.

Centraal Bureau Statistiek (2010). Population: key figures, 05 April 2013. Retrieved from: http://statline.cbs.nl/StatWeb/publication/default.aspx?VW=T&DM=SLEN&PA=37296eng&D1=a&D2=0%2c10%2c20%2c30%2c40%2c58-59&HD=090302-1045&LA=EN&HDR=G1&STB=T

Consumer health informatics research resource. Subjective norm. Consulted on Dec 2013, 1st. Retrieved from : http://chirr.nlm.nih.gov/subjective-norm.php

Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York: Psychology Press.

Newbold et al. (2013), Statistics for Business and Economics, Pearson Education Limited

Popov, S. (2012). The choice of an electricity tariff at one-person household. Rotterdam school of management, Rotterdam, The Netherlands.

UCGS (2013). Snowball Sampling. Consulted on Dec 2013 1st, Retrieved from: http://www.fort.usgs.gov/landsatsurvey/SnowballSampling.asp

Verhagen, E. (2012). The impact of framing on consumer selection of energy tariffs. Rotterdam school of management, Rotterdam, The Netherlands.

17

APPENDIX I: Online survey form

Online survey: Real-time tariffs

Please take a few minutes to tell us what you think about the possibility of switching from flat

energy tariffs to real time tariffs. There is no right or wrong answers; we are merely interested

in your personal opinions. In response to the questions below, please list the thoughts that

come immediately to your mind.

We will start with general information in order to evaluate your profile and we will then move

on to the main topic of our subject: to assess the willingness of Dutch people to switch to real-

time tariffs.

This second part of the survey is a scale survey. You will be presented several affirmations.

You should rate them from 1 to 6 with 1 meaning "I completely disagree" to 6 meaning "I

totally agree".

The information given will remain strictly confidential the statistical results however will be

used to produce a report on the subject.

Thank you for your participation.

Haut du formulaire

1.What is your age?

2.What is your gender?

o Female

o Male

3.Where were you born?

4.Where were you raised?

5.In which city of the Netherlands do you currently live?

6.What is your level of education?

18

7.I am never at home on weekdays between 9am to 5pm

1 2 3 4 5 6

Select a value from a range of 1 to 6.

8.Using renewable energy does not make any difference to me

1 2 3 4 5 6

Select a value from a range of 1 to 6.

9.Using renewable energy in my household would make me feel better about myself

1 2 3 4 5 6

Select a value from a range of 1 to 6.

10.If all my family/friends switched from flat energy tariffs to real time tariffs, I would do so

as well

1 2 3 4 5 6

Select a value from a range of 1 to 6.

11.I would stick to my daily routine even if it costs me money

1 2 3 4 5 6

Select a value from a range of 1 to 6.

12.I would agree to program the utilisation of my dishwasher with a time switch

19

1 2 3 4 5 6

Select a value from a range of 1 to 6.

13.I have strict daily routines

1 2 3 4 5 6

Select a value from a range of 1 to 6.

14.Using renewable energy is not worth the price I would have to pay

1 2 3 4 5 6

Select a value from a range of 1 to 6.

15.According to my family/friends, it is very important for me to help the environment as

much as I can

1 2 3 4 5 6

Select a value from a range of 1 to 6.

16.Most people who are important to me would disapprove of my switch from flat energy

tariffs to real time tariffs

1 2 3 4 5 6

Select a value from a range of 1 to 6.

17.If all my neighbors switched from flat energy tariffs to real time tariffs, I would do so as

well

20

1 2 3 4 5 6

Select a value from a range of 1 to 6.

18.I am confident that I can switch from fixed to real time tariffs without major

problems/without major discomfort

1 2 3 4 5 6

Select a value from a range of 1 to 6.

19.Most people like me would switch from flat energy tariffs to real time tariffs

1 2 3 4 5 6

Select a value from a range of 1 to 6.

20.Whether the energy used in my household is renewable is of no concern to me

1 2 3 4 5 6

Select a value from a range of 1 to 6.

21.I think the government should force people to switch from flat energy tariffs to real time

tariffs

1 2 3 4 5 6

Select a value from a range of 1 to 6.

22.My degree of concern about using renewable energy influences my decisions about energy

consumption

1 2 3 4 5 6

21

Select a value from a range of 1 to 6.

23.I am a flexible person

1 2 3 4 5 6

Select a value from a range of 1 to 6.

24.If I was given the opportunity, I would intend to switch from flat energy tariffs to real time

tariffs

1 2 3 4 5 6

Select a value from a range of 1 to 6.

25.I don't have enough money to use renewable energy

1 2 3 4 5 6

Select a value from a range of 1 to 6.

26.I separate my trash

1 2 3 4 5 6

Select a value from a range of 1 to 6.

27.I think that real-time tariffs is a good idea

1 2 3 4 5 6

Select a value from a range of 1 to 6.

28.I am concerned with the environment and the future of the planet.

22

1 2 3 4 5 6

Select a value from a range of 1 to 6.

Appendix II: Interview quotes

I just switched from [energy provider] Nuon to essent. I switched to essent because somebody

told me it is the cheapest one and it also has green energy.

Well I still think the idea behind the real time tariffs is not good. Because making it more

expensive during dinner time, you can’t expect people at work all day to change their

schedule.

[My friends] are not members of green peace, but there is knowledge about it. I can’t say that

they all are in favor of it or use it but they know that it is important.

For us [real time tariffs] will not be cheaper to go up during those peak hours which is in the

morning and after 17. So we will probably not use the low prices during those hours because

we are just not at home.

I think there always has to be a choice. Some people don’t have a choice if they work on a 9 to

17 basis. So it’s ok to implement [real time tariffs] but then there has to be a reward as well

because they will have a higher cost of living.

I think it’s important to educate children for a better environment

I think that it’s normal for the people from the villages [to be aware of environmental issues]

as they ‘re closer to nature, they see more green in their daily lives. People, children raised in

cities, they don’t care too much, because they see trash all the time.