2012
Erasmus University
Rotterdam School of Management
MSc. Business Information Management
Emanuela Rodica Verhagen
Student no. 324777
Co-reader: Dr. Laurens Rook
Co-reader: Dr. Jan van Dalen
Coach: Dr. Wolfgang Ketter
December 2012
[THE IMPACT OF FRAMING ON CONSUMER
SELECTION OF ENERGY TARIFFS]
[Abstract: It is becoming increasingly more important for the energy industry to promote the switch from
traditional to renewable energy. Research was done so far mainly regarding the role of the attitude towards
renewable energy in the switch to more sustainable energy sources. This paper aims to study the impact of
framing on the energy tariff choice and reveals the framing that most effectively promotes energy dynamic prices,
which can better sustain the new energy smart grid. The findings of the proposed meta-analysis of three
behavioral experiments confirm that the positive attribute framing is the most successful in convincing people to
choose for dynamic prices. Moreover, the risky choice framing is particularly effective for people with a high risk
taking attitude and the goal framing for people with a relatively less positive attitude towards renewable energy.
This indicates that framing effects have an impact on tariff choice and that through the use of frames we can
promote dynamic prices.]
1
Preface
The copyright of this Master thesis rests with the author. The author is responsible for its
contents. RSM is only responsible for the educational coaching and cannot be held liable for the
content.
2
Table of Contents
Preface ............................................................................................................................................ 2
Abbreviation List ............................................................................................................................. 6
1
2
3
Introduction ............................................................................................................................ 7
1.1
Background....................................................................................................................... 7
1.2
Research Question ........................................................................................................... 9
1.3
Gap in Research.............................................................................................................. 11
Related Literature ................................................................................................................. 12
2.1
Energy Tariffs.................................................................................................................. 12
2.2
Prospect Theory ............................................................................................................. 14
2.3
Framing........................................................................................................................... 14
2.4
Attitude towards Renewable Energy ............................................................................. 20
2.5
Gray's Theory of Brain Functions and Behavior - BIS/BAS Measure.............................. 21
Conceptual Model ................................................................................................................. 23
3.1
4
Focal Unit and Domain ................................................................................................... 24
Methodology......................................................................................................................... 26
4.1
Three experiments ......................................................................................................... 26
4.1.1
Structure of the Experiment ................................................................................... 27
4.1.2
Content of the Frames ............................................................................................ 30
4.2
Pilot Test ......................................................................................................................... 34
4.3
Controlling the noise ...................................................................................................... 34
4.4
Analyses.......................................................................................................................... 35
4.4.1
Basic Framing Analysis ............................................................................................ 37
3
5
4.4.2
Conjoint Analysis ..................................................................................................... 39
4.4.3
Categorical Principal Component Analysis ............................................................. 40
Results ................................................................................................................................... 41
5.1
5.1.1
Experiment 1: Risky Choice Framing....................................................................... 41
5.1.2
Experiment 2: Attribute Framing ............................................................................ 41
5.1.3
Experiment 3: Goal Framing ................................................................................... 41
5.2
6
Descriptive Analysis of the Experiments ........................................................................ 41
Impact of Framing on Tariff Selection ............................................................................ 43
5.2.1
Experiment 1: Risky Choice Framing....................................................................... 43
5.2.2
Experiment 2: Attribute Framing ............................................................................ 44
5.2.3
Experiment 3: Goal Framing ................................................................................... 46
5.3
Analysis of Individual Preferences for Tariff Attributes ................................................. 48
5.4
Analysis of Tariff Proximity under Different Framing .................................................... 51
5.4.1
Global View of the Categorical Principal Components Analysis ............................. 53
5.4.2
Explanation of Dimension 2 .................................................................................... 55
5.4.3
Explanation of Dimension 1 .................................................................................... 55
5.4.4
Tariff Proximity........................................................................................................ 57
General Discussion and Conclusion ...................................................................................... 59
6.1
Theoretical Relevance .................................................................................................... 59
6.1.1
Discussion of the Separate Framing Experiments .................................................. 60
6.1.2
Discussion of the Two Moderators in the Present Research.................................. 61
4
6.1.3
7
Discussing the Meta-analysis .................................................................................. 61
6.2
Managerial Implications ................................................................................................. 62
6.3
Limitations ...................................................................................................................... 64
6.4
Further Research ............................................................................................................ 65
6.5
Conclusion ...................................................................................................................... 65
References ............................................................................................................................ 66
Appendix 1: Example of Experiment............................................................................................. 74
Appendix 2: CAPCA Output ........................................................................................................... 84
Appendix 3: Vector Angles ............................................................................................................ 85
Appendix 4: List of Tables ............................................................................................................. 86
Appendix 5: List of Figures ............................................................................................................ 87
5
Abbreviation List
TOU = time of use
RTP = real time pricing
CATPCA = Categorical Principal Component Analysis
ANOVA = Univariate Analysis of Variance
Etc. = etcetera
6
1 Introduction
Never were the governments so concerned about sustainability and the energy usage of the
population as they are today. The recent disaster involving the Fukushima Daiichi nuclear plant
in Japan created an explosion of radioactive material (Black, 2011) and the explosion of one of
the four reactors of the Chernobyl power station in 1986 was reported by Greenpeace to have
caused thousands of thyroid cancers (McKeating, 2011). These are only two examples of
disasters that happened due to the use of nuclear energy. The raising concerns about
environmentally friendly products and the rising prices for oil, gas, electricity, coal, and
resources, are additional reasons why governments (especially those of developed countries)
intend to improve the way energy is created, used as well as distributed. As a consequence,
there is the need to make the energy system safer and more efficient.
1.1 Background
Due to the Fukushima disaster, the German government announced that the country’s nuclear
plants would be closed by 2022 and would switch to more durable and sustainable energy
through the smart grid (Evans, 2011). Other countries are likely to follow Germany’s example in
their search for safer and sustainable energy. This is due to the fact that its initiative is based
also on sound research that shows that if existing green technologies would be used to their
fullest by 2020, consumers could use 90% less energy (i.e. gas, electricity) from the grid than
they do today (Busnelli, Shantaram & Vatta, 2011).
While governments are willing to change the way energy is created and support companies that
produce sustainable energy, the key question is whether the consumers are going to agree with
it through their behavior and whether they are going to choose the pricing that supports the
distribution of sustainable energy. Nowadays, there are multiple types of pricing tariffs which
are more or less supporting the distribution of sustainable energy. But, will consumers choose
the tariffs that support the smart grid and will they do that even though this may mean taking
more risk?
7
It is highly likely therefore that the existing energy grid, designed to distribute energy from few
large plants that constantly generate power, will be exchanged for a decentralized, small unit
smart grid with variable capacity (Jansen et. al, 2005). This results in a need for high
investments in storing the energy in order to compensate for the fluctuations (Gottwalt et al.,
2011). However, this does not need to be the case if another strategy is in place that convinces
people to consume energy when much energy is in place (at times when wind, sun and other
sources are strong) and not to consume much when the energy that can be produced in
sustainable ways cannot be produced in large quantities. In order to create this incentive,
variable prices are highly preferred by energy companies. These variable prices transfer a part
of the risk of weather conditions to the energy consumers. By choosing variable prices that
change according to weather conditions, the consumer is motivated to use energy in a way that
benefits the energy companies. Thus, when sustainable energy sources will be scarce (e.g. no
sun, slow wind, slow movement of water, etc.) this will result in a higher price and people will
have an incentive to use less energy. However, will the consumers choose these variable pricing
tariffs that come from green energy? And how can energy companies convince consumers to
choose variable prices? In what way should they advertise their energy tariffs? And to what
extent does the way the information is explained have an impact on the decision making
behavior of the energy consumers?
Figure 1 Eneco Source of Electricity
8
Eneco Netherlands already adopted 100% green electricity delivery (Eneco, 2012a). The
company is becoming more and more dependent on the weather conditions, since around 28%
from Eneco’s electricity comes from wind or sun; see Figure 1 (Eneco, 2012a). The company
experiments with variable prices at a low scale by offering contracts to consumers with prices
that change every half a year (Eneco, 2012a). Moreover, Eneco is also trying to convince
consumers in the future to choose for a more variable pricing tariff by providing them with the
TOON tablet that shows more information about their consumption as well as weather
conditions (Eneco, 2012b).
It seems that through hourly variable prices the supply and demand of energy will truly be
balanced and the high costs of stocking the energy will be reduced considerably or even
eliminated completely (Gottwalt et al., 2011). Therefore, along with the half year variable
tariffs, hourly variable tariffs are likely to be added in the choice list for consumers, since the
energy companies continue to rely more and more on wind energy and other variable sources.
However, the question remains as to whether the consumer is more willing to participate in this
smart grid in certain contexts, i.e. frames, by actually choosing variable price tariffs and tariffs
that come from green energy. Another question that arises from the previous question is
whether energy companies can influence this decision making process by adopting advertising
strategies and by putting the information they give about their list of tariffs in different
contexts; i.e. frames. This paper aims to support energy providers like Eneco find out ways in
which to convince people to choose for dynamic prices when dynamic prices will be in place.
Truly dynamic prices that change every hour, every day based on weather conditions and
consumers’ demand are not in place yet. This paper offers an important support for energy
companies in providing them with information about how to start advertising for dynamic
prices.
1.2 Research Question
There is a growing interest concerning sustainable energy consumption, ranging from
engineering techniques to generate renewable energy to policies aimed at encouraging private
households, businesses and countries to use renewable energy. A major challenge in that
9
respect is how to facilitate the transition among energy consumers from traditional to
renewable energy, and how to promote the selection of services that support the consumption
of sustainable energy in markets with dynamic energy prices (Gottwalt et al., 2011; Weerdt et
al., 2011). Up till now, scholars have mainly explored consumers’ attitude towards renewable
energy in predicting their willingness to pay extra for renewable energy (Bang et al., 2000; Yoo
& Kwak 2009) 1.
In the present paper, however, we argue that we should also focus on the issue of how to
formulate the information concerning (renewable) energy tariffs. This so-called “framing” of
messages is proven to have an impact on the formation of individual beliefs and preferences in
all sorts of decision making and consumer behavior (Tversky & Kahneman et al., 1981), and
could also exert an influence on sustainable energy consumption. To the best of our knowledge,
the impact of framing on energy tariff selection has not yet been investigated. This is
unfortunate because we can more effectively promote sustainable energy consumption and
dynamic pricing once we identify how energy-related framing best convinces consumers to
choose for dynamic pricing and renewable energy.
If the energy companies knew what makes customers choose for hourly variable price tariffs
and green tariffs, then they would know how to advertise the variable tariffs and to whom.
Therefore, analyzing what characteristics of a person makes that person more willing to choose
a variable tariff will also help the energy providers know whom to advertise to. Moreover, if the
energy providers knew what makes a consumer choose variable tariffs or green tariffs, this
would help them advertise the variable prices in a better, more attractive way. These matters
are the main focus of the paper, which is twofold: What are the effects of framing and of the
risk taking attitude of the consumer on energy tariff selection and to what extent does the tariff
selection depend on the riskiness of a tariff (variable, flat, etc.) and its degree of greenness?
1
Please note that parts of this paper were published in the article “The Impact of Framing on Consumer Selection
of Energy Tariffs” (Verhagen et. al, 2012) as proceedings of the Smart Grid Technology, Economics and Policy
Conference.
10
1.3 Gap in Research
There are few papers that are written about the determinants of the choice of consumers for a
specific energy tariff. Whereas there is research done that shows that people are willing to pay
extra for renewable energy (Bang, 2000; Yoo & Kwak, 2009), not much was researched about
what type of tariff the consumer would prefer to have: flat tariff, time of use tariff or variable
price tariff and green or grey tariff. What is interesting to observe is how different ways of
providing the same information about tariffs will influence the consumer to choose a tariff
above another. Popov (2012) came up with several determinants (regulatory focus, risk taking
attitude, need for cognition and attitude towards renewable energy) out of which the attitude
towards renewable energy and need for cognition were found in his sample to have an impact
on the consumer’s choice of tariffs (either renewable energy or normal energy). Surprisingly,
risk preferences did not yield significant effects on tariff choice. This paper comes to improve
the proposed conceptual model and methodology by going further and looking at different
frames in which the information about tariffs can be explained. Additionally, this paper is
introducing
two
different
theories
on
risk:
Prospect
Theory
and
Behavioral
Inhibition/Behavioral Activation Approach. Instead of going broad as the previous research and
not finding significant relationships, the current research is going deeper and analyses the
influence of the way the tariff is presented on the choice of the consumer.
In the rest of the paper, the literature related to the above research question will be discussed
and a set of hypotheses will be proposed. Following, the methodology of the conducted
experiment and the used analyses will be presented. The paper will end with showing the
results of the conducted experiment and providing the reader with conclusions, managerial
implications for the energy companies and recommendation for future research.
11
2 Related Literature
Energy markets are undergoing radical structural changes due to developments aimed at
implementation of renewable sources of energy. In the remainder of this paper, we will argue
that these developments change the energy domain into an uncertain and risk-prone market
environment, in which the issue of whether to accentuate the positive or the negative
consequences of future energy consumption becomes the more urgent. Based on the
confirmation for framing effects in behavioral economics it will be maintained that three
valence-based frames in particular can be employed to convince energy consumers to select
dynamic and renewable tariffs.
2.1 Energy Tariffs
Energy markets currently mostly use fixed energy tariffs. Even though energy consumption in
these systems is relatively insensitive to fluctuations in energy prices, this situation is rapidly
changing as a result of current developments. This forces the industry to allow for an increasing
supply of sustainable energy that comes from resources such as wind, water and sun (Wolak,
2003). Sustainable energy is highly dependent on fluctuations in weather conditions, which
makes the supply and demand for this type of energy more uncertain and more prone to risk of
overloading the network. A next generation of flexible energy tariffs deals with these
imbalances in the electricity market. At present, two types of energy tariffs reflect these new
dynamics. First, moderately risky time of use tariffs have different prices for each hour of the
day. Second, riskier real time pricing tariffs differ per day according to wholesale market and
weather conditions (Gottwalt et al., 2011; Weerdt et al., 2011). Importantly, the prices of real
time pricing tariffs must be understood as fluctuating with serious consequences for individual
households.
There are different types of energy tariffs: flat rate, seasonal rate, time of use rate, critical peak
pricing low rate, critical peak pricing high rate, real time pricing day ahead rate and real time
pricing real time rate (Faruqui & Wood, 2008a); see Figure 2. Each of these tariffs has a
different level of price volatility. Flat rates have the lowest price volatility and real time pricing
rates have the highest price volatility. In addition, each of these tariffs can be either green or
12
grey. Green energy comes 100% from renewable energy, which is created with the help of
wind, sun or other natural resources. The paper focuses on three types of tariffs: flat tariff, time
of use tariff and real time pricing tariff, each of these being either green or grey. This means
that there are a total of six tariffs discussed in this paper: grey flat tariff, green flat tariff, grey
time of use tariff, green time of use tariff, grey real time pricing tariff and green real time
pricing tariff.
The flat tariff and the real time pricing tariff were chosen because they have the corresponding
extremes of price volatility (100% price volatility and 0% price volatility). The time of use tariff
was chosen because it is more or less the middle point of the transition from flat tariff to real
time pricing tariff. Below, the three tariffs will be explained.
Figure 2: Hedging Cost Premiums (Source: Faruqui & Wood 2008a)
The main characteristic of the flat tariff is that the price is the same for every hour of the day.
No matter when people use appliances, the price they pay is all the time the same. For the time
of use tariff, the prices are higher during high-peak hours e.g. 6am, 7am, 5pm, 6pm, etc.,
slightly lower during mid-peak hours e.g. from 8am until 3pm and even lower during off-peak
hours e.g. from 10pm until 5am. Another characteristic of the time of use tariff is that the
different prices are the same every day of the week. The real time pricing tariff has the same
three-hour intervals (high-peak, mid-peak and off-peak), but the prices differ each day
according to wholesale market and weather conditions. Energy users get to know one day in
advance the price for each hour of each day, so that they will know when is the lowest price to
start their appliances such as their washing machine. Figure 3 gives an example for each of the
13
six tariffs. The grey and green tariffs in this paper have the same price due to the fact that
several energy companies do not make a difference of price between the two.
Figure 3: The Six Types of Energy Tariffs
2.2 Prospect Theory
Prospect theory is the most popular theory with regards to risk and the decision making process
of individuals. Several theories are based on the prospect that originates from the year 1979
when Tversky and Kahneman wrote their first paper on this theory. Simply and straightforwardly described, they induce risk aversion in the area of gains and risk seeking in the area
of losses (Tversky & Kahneman, 2000). Individuals are risk seeking when they are willing to be
involved in actions with a higher degree of risk and they are risk averse when they would rather
avoid risk. The theory states therefore that individuals generally become more risk averse when
they could gain more money, and they become more risk seeking when they are faced with
losses. Therefore, the way the information is presented could influence the extent to which the
individuals are more risk averse. On the foundation of this principle, several other theories are
developed, two of which are the Framing paradigm and the Gray's Theory of Brain Functions
and Behavior.
2.3 Framing
Framing builds upon the prospect theory (Kahneman and Tversky, 1979; Tversky and
Kahneman, 1992; Starmer, 2000), which in turn builds upon the expected utility theory
14
(Bernoulli, 1738; Wakker, 2010). The framing approach suggests that the consumer will be
influenced in his choice behavior by the way the information about these choices is framed
(Tversky and Kahneman, 1981). Even though this approach has been officially developed by
Tversky and Kahneman (1981), the principle of this theory is old and used by all ranges of
people. For ages people have used different framings and techniques in which they attempted
to influence the decision making of others by presenting in a more attractive way an option that
they would prefer others to choose. Levin et al. (1998) looks into three types of framing: risky
choice framing, attribute framing and goal framing; see Table 1.
Table 1: Summary of Methodological Differences in Risky Choice, Attribute and Goal Framing - Source: Levin et al. 1998
Frame Type
What is framed
What is affected
How effect is measured
Risky choice
Set of options with
Risk preference
Comparison of choices for
different risk levels
Attribute
Object/event attributes or
risky options
Item evaluation
characteristics
Comparison of
attractiveness ratings for
the single item
Goal
Consequence or implied
goal of a behavior
Impact of persuasion
Comparison of rate of
adoption of the behavior
The framing paradigm states that the risky choice framing will affect the preference of the
respondent for each of the presented options. The attribute framing will affect the evaluation
that the respondent will give on a presented item and the goal framing will affect the behavior
of the respondent; see Table 1. Each of these frames will be discussed in more detail below.
However, before each type of framing is explained, it is important to mention what they all
have in common: the negative and positive frame. In the negative frame, the choices, the
object or the behavior is presented with negative words (e.g. lost, failure, loss), while in the
positive frame, it is presented with positive words (e.g. saved, success, gain).
Framing effects are seen when people’s decision making process is influenced by the way in
which the options are presented. The “Asian disease problem” is the classic example of a
framing effect (Tversky & Kahneman et al., 1981), and was introduced as follows:
15
“Imagine that the United States is preparing for an outbreak of an unusual Asian disease that is
expected to kill 600 people. Two alternative programs to combat the disease have been
proposed. Scientific estimates of the consequences of the programs are:

Positive frame. If Program A is adopted, 200 people will be saved. If Program B is
adopted, there is a one-third probability that 600 people will be saved and a two-thirds
probability that no people will be saved.

Negative frame. If Program C is adopted, 400 people will die. If Program D is adopted,
there is a one-third probability that 600 people will be saved and a two-thirds
probability that no people will be saved. “ (Tversky & Kahneman et al., 1987, pp. 74-75)
In the Asian disease problem, the two problem alternatives per frame are identical – but
programs A and C represent a sure bet, while programs B and D represent a gamble. The classic
finding is that participants usually select program A or D, depending on the positive or negative
valence of the choice frame. When people are confronted with a risky choice, they are more
likely to engage in risk-seeking in the face of a negative frame, but be risk averse in the face of a
positive frame. Even though this risky choice framing effect has been replicated in hundreds of
studies, Levin et al. (1998) pointed to the sometimes contradictory findings within this
paradigm. The authors suggested that scholars should also take into account the potential
effects of attribute framing and goal framing. Attribute framing captures situations in which
people evaluate an object or event based on its characteristics, such as price, product features
or success/failure rate. In one study, for instance, participants were asked to state their
preferences for ground beef, which was either presented as being ”75% lean” or ”25% fat”
(Levin et al., 1998). People appeared more willing to consider an option when its attributes
were presented in positive rather than negative terms. Goal framing describes situations in
which the goal of an action or behavior is framed. Then, people choose between options that
either emphasize the goal of obtaining success or gains, i.e. the positive consequences of a goal,
or the goal of avoiding failure or losses, i.e. the negative consequences of a goal. Exposing
people to a loss frame had greater impact on their decision making process than providing
them with a gain frame (Kahneman et al, 1990).
16
The first framing paradigm of “risky choice” says that framing a risky option in a more positive
light, e.g. 70% saved lives, makes the decision maker prefer that option less than if the risky
choice would be framed in a more negative light, e.g. 30% lost lives. As Levin et al. (1998, p.
181) wrote, “positive frames generally enhance risk averse responding relative to negative
frames”. In the negative frame people tend to seek risk, and therefore choose for the risky
choice, while in the positive frame people tend to avoid risk, and therefore choose for the sure
choice. Figure 4 shows an example of this framing. In this example, the risky choice in the
positive frame, i.e. Chance all saved with chance none saved, will be chosen less often than the
same risky choice that is presented in a negative frame, i.e. Chance all lost with chance none
lost.
Positive
frame
Negative
frame
Sure thing
Risky
option
option
Some saved
for sure
Chance all
saved with
chance none
saved
Sure thing
Risky
option
option
Some lost for
sure
Chance none
lost with
chance none
saved
Preference
Compare to determine
framing effect
Preference
Figure 4: Risky Choice Framing
The risky choice framing paradigm predicts that people will display a general preference for
risky, i.e., flexible and green, rather than safe, i.e., fixed and gray, energy tariffs under a
negative frame. In that case, any energy tariff that resembles a gamble will be favored over a
sure bet. Under a positive frame, however, people will select the energy tariff that resembles a
safe bet rather than a gamble. This means that for the energy market, people would tend to
prefer the more risky tariffs when the information about all tariffs will be presented with
17
negative words. In this negative framing, the respondents will tend to prefer more often the
real time pricing tariffs (Gottwalt et al., 2011; Weerdt et al., 2011) over the time of use tariffs,
and prefer the time of use tariffs over the flat tariffs. Under the same negative frame, people
will tend to prefer more green tariffs than under the positive frame, because the green tariffs
are perceived to be more risky than the grey tariffs. More formally:
Hypothesis 1: People will prefer riskier energy tariffs under a negative choice frame, but surer,
i.e., less risky, energy tariffs when under a positive choice frame.
Unlike the risky choice framing paradigm that is about showing preference for different options,
in the attribute framing the decision maker has to evaluate one specific object. One attribute of
the object is framed either positively or negatively.
Object or
event
Positive
% Success
Evaluation
frame
Object of
event
Negative
Compare to determine
framing effect
% Failure
Evaluation
frame
Figure 5: Attribute Framing
The object will be more favorably evaluated when it will be framed positively than if it would be
framed negatively since the positive framing stimulates positive associations and negative
framing stimulates the negative associations. Figure 5 shows an example of attribute framing.
In this example, people tend to respond more favorably to the object or the event when the
18
probability of its success will be shown than when the probability of its failure will be shown; 1
minus probability of success.
The attribute framing paradigm predicts that people display a preference for positively rather
than negatively presented product features – such as price, switching costs, or percentage of
successful adoptions. Applied to the green real time pricing tariff, which is a particularly
promising in sustaining the smart grid (Gottwalt et al., 2011; Weerdt et al., 2011), it seems
reasonable to assume that consumers will choose in favor of this tariff if its attributes are
presented positively rather than negatively. Moreover, if the attribute framing is true for the
energy tariffs, then this will mean that energy consumers will tend to evaluate the real time
pricing tariff better when it will be described with a success percentage, i.e. n%, rather than a
failure percentage, i.e. (1-n)%, which gives in fact the same information but in two different
frames. This is because the people are more willing to choose a cup that is half full than a cup
that is half empty. More formally:
Hypothesis 2: People will prefer a positively presented green real time pricing tariff over a
negatively presented green real time pricing tariff given the factual information.
Goal framing is different from the attribute framing as it seems to have the oposite effect, since
the negative frame would have more influence on choosing a certain behavior than the positive
frame. The negative frame gives a higher likelihood of performing an act than positive frame.
The positive frame invokes the goal of obtaining gain, while the negative frame invokes the goal
of avoiding loss (Levin et al, 1998). When people are provided with the negative consequences
of not having a certain behavior, they will be more inclined to have that behavior, than when
they are provided with the positive consequences. It seems to show, therefore, that
“punishments”, i.e. negative consequences of not choosing behavior X, are more effective in
convincing people to have a certain behavior than its “rewards”, i.e. positive consequences of
choosing behavior X. Figure 6 shows more clearly and graphically this type of framing.
19
Behavior X
Positive
frame
[APPROACH]
Obtain gain
Rate of
behavior X
(approach
behavior X)
Behavior
Non-X
Compare to determine
framing effect
Suffer loss
Negative
frame
[AVOID]
(avoid behavior
non-X)
Rate of
behavior X
Figure 6: Goal Framing
The goal framing paradigm predicts that if an exact same option is presented in terms of a gains
versus a loss, the loss frame will have higher impact. Obtaining a good is not valued as highly as
avoiding its loss – i.e., this is so-called endowment effect (Kahneman et al., 1990). For energy
tariff selection, it would therefore make sense if consumers would prefer green real time
pricing if information concerning this tariff is presented in terms of how the potential loss of
not choosing this tariff can be avoided. People will not be persuaded if the tariff-related
information emphasizes how selecting green real time pricing will help obtaining a gain. More
formally:
Hypothesis 3: Under a negative goal frame, people will choose the green real time pricing tariff
more often than under a positive goal frame.
2.4 Attitude towards Renewable Energy
The Renewable Energy Policy Network for the 21st Century (2011) mentioned in one of their
reports that 16% of global energy consumption comes from renewable energy sources. Green
energy is growing in popularity as previous research shows that several people are willing to
pay for renewable energy (Batley et al., 2001; Bergman, Hanley, & Wright, 2006; Borchers,
20
Duke & Parson, 2007). Moreover, the attitude towards renewable energy was found to have an
impact on the choice of tariffs a consumer would choose (Popov, 2012). Other research shows
that a positive attitude towards renewable energy impacts positively the willingness to pay for
green energy (Hansla et al., 2008). This paper goes further to look at whether the attitude
towards renewable energy influences the impact framing has on tariff selection.
In the paper, the following hypotheses with regards to attitude towards renewable energy will
be researched. The stronger the positive attitude towards renewable energy will be, the
stronger the relationship between framing and tariff selection will be:
Hypothesis 4: The more positive the attitude towards renewable energy of a person is, the
stronger the relationship between the risky choice framing and tariff selection will be.
Hypothesis 5: The more positive the attitude towards renewable energy of a person is, the
stronger the relationship between the attribute framing and tariff selection will be.
Hypothesis 6: The more positive the attitude towards renewable energy of a person is, the
stronger the relationship between the goal framing and tariff selection will be.
2.5 Gray's Theory of Brain Functions and Behavior - BIS/BAS Measure
Inspired by Grey’s Theory of Brain Functions and Behavior, several studies used the BIS/BAS
measure that reflects two general motivational systems (Carver & White, 1994) that are
predictive of people’s general risk seeking versus risk avoiding tendencies. Specifically, the
theory distinguishes between a behavioral approach system (BAS) that is risk seeking and a
behavioral inhibition system (BIS) that is risk averse. Prior research has shown that BIS/BAS can
detect someone’s motivation to approach or avoid particular decisions in risky settings.
A scale was developed to assess the individual differences in the sensitivity of these two
concepts (Carver & White, 1994). The measures BIS and BAS of Carver and White’s paradigm
reflect the attitude of an individual towards risk. The purpose of BIS/BAS measurements is to
assess the dispositional sensitivity to the behavioral inhibition system (BIS), which is the risk
avoiding behavior, and the behavioral approach system (BAS), which is the risk taking behavior
(Carver & White, 1994). In other words, the BIS measure shows the risk averseness of an
21
individual and the BAS measure reflects the risk seeking of an individual. While the BIS measure
is a stand-alone concept i.e. anticipation of punishment, the BAS measure is formed out of
three concepts: anticipation of reward (Reward Responsiveness), pursuit of desired goals
(Drive) and desire for new rewards as well as impulsive approach to potential rewards (Fun
Seeking).
In the paper, the following hypotheses will be researched, with the help of an overall computed
BIS/BAS measure of risk averseness. This score is calculates as the average score from the BIS
questions minus average score from the BAS questions (Sherman et al., 2006). The higher the
overall computed BIS/BAS measure will be, the higher the risk averseness and the lower the risk
taking attitude of an individual.
Hypothesis 7: The more risk taking a person is, the stronger the relationship between the risky
choice framing and tariff selection will be.
Hypothesis 8: The more risk taking a person is, the stronger the relationship between the
attribute framing and tariff selection will be.
Hypothesis 9: The more risk taking a person is, the stronger the relationship between the goal
framing and tariff selection will be.
22
3 Conceptual Model
The study of the tariff choice selection has the purpose of answering the following set of
questions: What are the effects of framing and of the risk taking attitude of the consumer on
energy tariff selection and to what extent does the tariff selection depend on the riskiness of a
tariff (variable, flat, etc.) and its degree of greenness? This paper analyzes the concepts that
have an impact on the energy tariff choice that the consumer is making. The choice is made
among the flat tariff, which has the same price every hour of the day, the time of use (TOU)
tariff, which has different prices during the day, but they are the same every day and the real
time pricing (RTP) tariff which has variable prices that may change every hour, every day
according to the weather conditions. The preference, evaluation and choice of a tariff are here
the dependent concepts that can be summarized into one concept: tariff selection. The
independent concepts are the different frames: risky choice framing, attribute framing and goal
framing, each of them having a positive frame and a negative frame. The two moderator
variables are the risk taking attitude (being risk averse or risk taking) of an individual and the
attitude of an individual towards renewable energy. They are assumed to be interfering with
the relationship between the different frames and the tariff selection. Figure 7 shows the
conceptual model that is researched in this paper.
Renewable
energy attitude
x
Risky Choice
Framing
H7
H4
Preference for a tariff
H1
Renewable
energy attitude
Attribute
Framing
Risk taking
attitude
Risk taking
attitude
H8
H5
H2
Evaluation of a tariff
TARIFF
SELECTION
Renewable
energy attitude
Goal
Framing
Risk taking
attitude
H9
H6
Choice of a tariff
H3
Figure 7 Conceptual Model
23
From this conceptual model, nine hypotheses can be deduced that show the expected pattern
of this research; see Table 2.
Table 2: Hypotheses
XY where X={A, B, C} (therefore, X can be either A, B, C) and D–moderating concept
H1: People will prefer (Y) riskier energy tariffs under a negative choice frame (A),
but surer, i.e., less risky, energy tariffs when under a positive choice frame.
AY
H2: People will evaluate better (Y) a positively presented green real time pricing
tariff (B) over a negatively presented green real time pricing tariff given the factual
BY
information.
H3: Under a negative goal frame (C), people will choose (Y) the green real time
pricing tariff more often than under a positive goal frame.
CY
H4: The more positive the attitude towards renewable energy of a person is (D),
the stronger the relationship between the risky choice framing and tariff selection
will be.
D
AY
H5: The more positive the attitude towards renewable energy of a person is (D),
the stronger the relationship between the attribute framing and tariff selection will
be.
H6: The more positive the attitude towards renewable energy of a person is (D),
the stronger the relationship between the goal framing and tariff selection will be.
H7: The more risk taking (E) a person is, the stronger the relationship between the
risky choice framing and tariff selection will be.
H8: The more risk taking (E) a person is, the stronger the relationship between the
attribute framing and tariff selection will be.
H9: The more risk taking (E) a person is, the stronger the relationship between the
goal framing and tariff selection will be.
D
BY
D
CY
E
AY
E
BY
E
CY
3.1 Focal Unit and Domain
The focal unit of the study is the energy tariff, since it is the common denominator of all
variables (Pomrehn et al., 2011). An energy tariff is defined in this paper as a type of costing
structure based on which the customers need to pay their energy consumption to their energy
supplier. Moreover, the theoretical domain contains all the tariffs that are similar to any of the
24
researched tariffs or any combination of them: green tariff, grey tariff, flat tariff, time of use
tariff and real time pricing tariff.
25
4 Methodology
There may be multiple ways to find out how people choose a tariff. However, it is important to
know whether the way the tariffs are presented affects the consumer’s choice. Once the
influence of the context is known then more research can be built on top of it. The context that
is analyzed in the paper is the framing in which specific information is written. Therefore, this
research was based on different framing theories, but also on the prospect theory and Gray’s
Theory of Brain Functions and Behavior that facilitated the analysis of the impact of different
characteristics of people (i.e. attitude towards risk and risk taking attitude) on their decision
making. In short, the research analyzed the decision making of each type of person under each
type of framing. As discussed earlier, it is important to find out which type of framing has the
most significance in influencing the consumer to take more risk by choosing the green real time
pricing tariff. Once this aspect is known, the energy companies will know what framing strategy
to adopt in their advertisement program.
An experiment has been designed in order to find out the impact of different frames on the
decision making process of the energy consumers. While also a case study and a survey could
have been chosen, an experimental setting was chosen because it enabled us to draw
conclusions concerning causality (Hak, 2010). The added value of the present series of
experiments was that it enabled us to compare between the potential impact of each frame on
the same selection of energy tariffs.
In this case, the value of the independent concepts was manipulated into either positive or
negative frame, and the selected tariff was observed. Additionally, the risk taking attitude and
the attitude towards renewable energy of the respondents was measured in order to see
whether they are moderating concepts and whether they strengthen the relationship between
the frames and the observed tariff selection.
4.1 Three experiments
Three experiments were conducted: one experiment for each type of framing i.e. risky choice,
attribute and goal, that compared the frame’s positive and negative wordings. The respondents
were split evenly into six groups. The six groups of respondents were assigned according to the
26
independent concepts into six frames: two frames (positive vs. negative) for each of the three
types of framing (risky choice framing, attribute framing and goal framing). Each group
represented one case of the experiment. Table 3 shows the cases of the experiment.
Table 3: Cases of the Experiment
Experiment
Positive Frame Cases
Experiment 1: Risky Choice Framing =
Case 1.1.Risky Choice Framing +
+
Case 2.1.Risky Choice Framing -
Case 1.2. Attribute Framing +
+
Case 2.2.Attribute Framing -
Case 1.3. Goal Framing +
+
Case 2.3. Goal Framing -
Experiment 2: Attribute Framing =
Experiment 3: Goal Framing =
Negative Frame Cases
4.1.1 Structure of the Experiment
For each experiment, campus students were invited to volunteer in a 30 minute pencil- andpaper survey. Their structure consisted of an introduction, BIS/BAS questions (Cronbach’s α for
the BIS measure = 0,766 and Cronbach’s α for the BAS measure = 0,738), a rank ordering of
tariffs, the selection of a preferred tariff after exposure to a randomly picked frame, another
rank ordering of tariffs, and general attitudinal questions. In the introduction, participants were
presented with a description of general characteristics of energy tariffs in the marketplace: grey
or green flat tariff, grey or green time of use tariff and grey or green real-time pricing tariff.
Next, participants were invited to order the six energy tariffs according to their preferences by
giving a number between 1 and 100 to each tariff, with the lowest number representing their
least preferred option. Another rank-ordering question asked the respondents to order the six
tariffs based on their preference, giving 1 to the most preferred option and 6 to the least
preferred option. After this introductory stage, participants were randomly exposed to one of
three framing conditions. The framing conditions were presented as additional information to
the energy tariffs. Participants were then invited to select their preferred tariff from the six
energy tariffs they had previously been introduced to. Also, they were asked again the two
questions about the ordering of the six energy tariffs based on preference. Lastly, participants
were presented with questions about demographics and their attitude towards renewable
energy (Cronbach α = 0,852). In the end of the survey, participants were probed for awareness,
thanked and dismissed.
27
4.1.1.1 Risk Taking Attitude
The six cases of the experiment had the same structure and similar content. The experiment
assessed the respondents’ risk taking attitude and identified the degree to which the
respondent was risk seeking or risk averse. The measurement of the risk taking attitude was
derived from the BIS/BAS measures and questions (Carver & White, 1994). This method is used
in prospect theory and often in research concerning decision making.
The respondents were asked what their risk taking attitude was, by using a set of 24 statements
and asking the respondent to indicate to what extent they agree or disagree: “Even if
something bad is about to happen to me, I rarely experience fear or nervousness; Criticism or
scolding hurts me quite a bit; I feel pretty worried or upset when I think or know somebody is
angry at me; If I think something unpleasant is going to happen I usually get pretty worked up; I
feel worried when I think I have done poorly at something important” (Carver & White, 1994, p.
323). All BIS/BAS questions are in the first part of all experiments ; see Appendix 1. The answers
were measured on a four point Likert scale (Vagias, 2006), 1 meaning Very true for me and 4
Very untrue for me. This four point scale is used strategically in order to “force” the respondent
to make a narrower choice between the options. This four point scale is used in the original
measurement of the BIS/BAS measurement. Moreover, due to the high English level of the
questions, a Dutch version was added (Franken, 2002) next to the questions in English.
The risk taking attitude was computed as an overall BIS/BAS score that is calculated as the
average score from the BIS questions minus the average score of the BAS questions (Sherman
et al., 2006). The score results in a representation of risk averseness. Thus, the higher the score,
the lower the risk taking attitude.
4.1.1.2 Explanation of Tariffs and Framing Questions
The experiment continued with an explanation of all six tariffs (grey flat, green flat, grey time of
use, green time of use, grey real time pricing, green real time pricing), by giving an explanation
for their attributes (Attribute1- Degree or Risk: flat, TOU, RTP; Attribute2-Degree of Greenness:
9% green energy - grey, 100% green energy - green). After the explanation of the tariffs, each of
the six frames was introduced in each of the six cases of the experiment. Each frame had its
28
own questions that were taken from the literature. The content of the frames will be discussed
later on in next section and is summarized in Table 4 as well.
4.1.1.3 Ordering of Tariffs and Expressed Preference
After the frames were applied and the respondents were asked the questions specific to each
of the frames, they were asked to order the six tariffs in the order of their preference. The
respondents were asked to give a number 1 to the most preferred tariff, a 2 to the second most
preferred tariff, etc. and a 6 to the least preferred tariff (Day et al., 1991). Next to this, they
were asked to give a number from 1 to 100 to all six tariffs in terms of preference. A higher
preference meant a higher number. These two questions were asked twice: once before the
framing was given ,as a pretest or pre-measurement, and once after the framing was given, as a
posttest, in order to look at the impact of the frame on the individual. This ordering facilitates
the realization of the Conjoint Analysis and Categorical Principal Components Analysis that will
be explained later on.
4.1.1.4 Attitude towards Renewable Energy
The experiment measured the attitude towards renewable energy and identified the degree to
which the respondent was positive towards renewable energy. The measurement of
respondents’ attitude towards green energy was done based on the Theory of Reasoned Action
(Ajzen & Fishbein, 1980).
The respondents were asked about their attitude towards renewable energy based on six
statements, where they were asked to indicate to what extent they would agree or disagree:
Using renewable energy does not make a difference for me; Whether the energy used in my
household is renewable is of no concern to me; Using renewable energy is not worth the price I
would have to pay (Ajzen & Fishbein, 1980). The answers were measured on a seven point
Likert scale, 1 meaning Not at all true for me and 7 meaning Very true for me.
4.1.1.5 Ending Questions
Furthermore, the respondents were asked to say to what extent they believe the study was
stupid and boring. These two questions were asked in order to eliminate from the study the
29
respondents that believed to a high extent that the study was boring or stupid. In this way, the
surveys that were not answered seriously by the respondents could be eliminated. Using the
same reasoning, the respondents were asked to say whether they took breaks and if yes, how
many. In case a respondent would take several breaks, the respondents’ survey could be
spotted and eliminated. The experiment ended with a standard set of biographical questions
about age, gender, education, etc.
4.1.2 Content of the Frames
The frames were presented to the respondents on a separate page so that they might not
overlook the important additional information that constitutes the dependent concept of the
research. The three types of frames, risky choice framing, attribute framing and goal framing,
represent the independent concepts of the experiment and correspond to three different
experiments that measure the difference of impact of positive and negative framing on tariff
selection under each type of framing. Therefore they needed to be manipulated and allow as
little noise as possible, and preferably none. Each of the frames was manipulated by either
giving participants a positive frame, by using positive words, or a negative frame, by using
negative words. Before the manipulation of the frames began, the respondents were asked to
imagine that they have a current energy tariff and that they need to change to another tariff.
They were also asked to consider that their consumption was constant. This was done so that
the experiment would be as close as possible to reality where people do need to switch from a
tariff to another and not choose one tariff without having a prior tariff. Moreover, it was
necessary to mention that the consumption would be constant. This was added because
otherwise the respondent might have believed that the probabilities of paying more or less
money described in the frames were the result of different patterns of consumption.
30
Table 4: Content of Frames & Measurement of the Tariff Selection
Framing
Risky Choice
+
Imagine that you already have an energy tariff. Now the time comes for you to choose
another tariff. Please assume that your consumption remains the same also in the coming
years. The following tariffs are in comparison to your old tariff that you already have.
If you choose grey flat tariff, you will pay 300 euro per year less.
If you choose green flat tariff, there is a 90% probability that you will pay 333 euro and 33
cent less per year and a 10% probability that you will pay the same.
If you choose grey time of use tariff, there is an 80% probability that you will pay 375 euro
less per year and a 20% probability that you will pay the same.
If you choose green time of use tariff, there is a 70% probability that you will pay 428 euro
and 57 cent less per year and a 30% probability that you will pay the same.
If you choose grey real time pricing tariff, there is a 60% probability that you will pay 500
euro less per year and a 40% probability that you will pay the same.
If you choose green real time pricing tariff, there is a 50% probability that you will pay 600
euro less per year and a 50% probability that you will pay the same.
Risky Choice
-
Imagine that you already have an energy tariff. Now the time comes for you to choose
another tariff. Please assume that your consumption remains the same also in the coming
years. The following tariffs are in comparison to your old tariff that you already have.
If you choose grey flat tariff, you will pay 300 euro per year more.
If you choose green flat tariff, there is a 90% probability that you will pay 333 euro and 33
cent more per year and 10% probability that you will NOT pay more money.
If you choose grey TOU tariff, there is 80% probability that you will pay 375 euro more per
year and 20% probability that you will NOT pay more money.
If you choose green TOU tariff, there is a 70% probability that you will pay 428 euro and 57
cent more per year and 30% probability that you will NOT pay more money.
If you choose grey real time pricing tariff, there is a 60% probability that you will pay 500
euro more per year and 40% probability that you will NOT pay more money.
If you choose green real time pricing tariff, there is a 50% probability that you will pay 600
euro more per year and 50% probability that you will NOT pay more money.
Measurement of the Tariff Selection
Measurement of the preference for a tariff
Which of the tariffs would you favor?
Grey flat tariff
Green flat tariff
Grey time of use tariff
Green time of use tariff
Grey real time pricing tariff
Green real time pricing tariff
How confident are you with the choice you
made?
Completely unsure
Mostly unsure
Somewhat unsure
Neither sure nor unsure
Somewhat sure
Mostly sure
Completely sure
Measurement of the preference for a tariff
Which of the tariffs would you favor?
Grey flat tariff
Green flat tariff
Grey time of use tariff
Green time of use tariff
Grey real time pricing tariff
Green real time pricing tariff
How confident are you with the choice you
made?
Completely unsure
Mostly unsure
Somewhat unsure
Neither sure nor unsure
Somewhat sure
Mostly sure
Completely sure
31
Attribute +
Imagine that you already have an energy tariff. Now the time comes for you to choose
another tariff. Please assume that your consumption remains the same also in the coming
years. Here is some information about the real time pricing tariff. This is one of the tariffs
that you can choose from.
70% of the people that tried green real time pricing mentioned that choosing this tariff was a
success and that they paid less money per year by choosing this tariff.
Attribute -
Imagine that you already have an energy tariff. Now the time comes for you to choose
another tariff. Please assume that your consumption remains the same also in the coming
years. Here is some information about the real time pricing tariff. This is one of the tariffs
that you can choose from.
30% of the people that tried green real time pricing mentioned that choosing this tariff was a
failure and that they paid more money per year by choosing this tariff.
Measurement of the evaluation of a tariff
How would you evaluate this green real time
pricing tariff?
Very positively
Mostly positively
Somewhat positively
Neither positively nor negatively
Somewhat negatively
Mostly negatively
Very negatively
How confident are you with the choice you
made?
Completely sure
Mostly sure
Somewhat sure
Neither sure nor unsure
Somewhat unsure
Mostly unsure
Completely unsure
Measurement of the evaluation of a tariff
How would you evaluate this green real time
pricing tariff?
Very positively
Mostly positively
Somewhat positively
Neither positively nor negatively
Somewhat negatively
Mostly negatively
Very negatively
How confident are you with the choice you
made?
Completely sure
Mostly sure
Somewhat sure
Neither sure nor unsure
Somewhat unsure
Mostly unsure
Completely unsure
32
Goal +
Imagine that you already have an energy tariff. Now the time comes for you to choose
another tariff. Please assume that your consumption remains the same also in the coming
years. Here is some information about the real time pricing tariff. This is one of the tariffs
that you can choose from.
Green real time pricing programs using real-time or day-ahead hourly energy rates offer
residential customers innovative choices in how they pay for power. Market-based rates
allow customers to adopt more energy efficient behaviors, and PROVIDE CONSUMERS WITH
TOOLS TO PAY LESS MONEY PER YEAR by using cheaper, off-peak power. So, if you want to
save money on energy choose real time pricing now!
Goal -
Imagine that you already have an energy tariff. Now the time comes for you to choose
another tariff. Please assume that your consumption remains the same also in the coming
years. Here is some information about the real time pricing tariff. This is one of the tariffs
that you can choose from.
Green real time pricing programs using real-time or day-ahead hourly energy rates offer
residential customers innovative choices in how they pay for power. Market-based rates
allow customers to adopt more energy efficient behaviors, and PREVENT CUSTOMERS FROM
PAYING MUCH MORE MONEY PER YEAR THAN THE OTHER TARIFFS by using cheaper, offpeak power. So, if you want to avoid paying more money with conventional tariffs, choose
real time pricing now!
Measurement of the choice of a tariff
After you have been reading this, will you
choose the green real time pricing tariff?
Strongly agree
Agree
Somewhat agree
Neither agree nor disagree
Somewhat disagree
Disagree
Strongly disagree
How confident are you with the choice you
made?
Completely sure
Mostly sure
Somewhat sure
Neither sure nor unsure
Somewhat unsure
Mostly unsure
Completely unsure
Measurement of the choice of a tariff
After you have been reading this, will you
choose the green real time pricing tariff?
Strongly disagree
Disagree
Somewhat disagree
Neither agree nor disagree
Somewhat agree
Agree
Strongly agree
How confident are you with the choice you
made?
Completely sure
Mostly sure
Somewhat sure
Neither sure nor unsure
Somewhat unsure
Mostly unsure
Completely unsure
33
4.2 Pilot Test
Before the six cases of the experiment were administered to the respondents, a pilot test was
conducted. The participants in the pilot test were asked to answer the questions from the
experiment. Seven individuals participated in this pretest. After the pretest was done, they
were enabled to freely express their impressions. After an open talk about the experiment, they
were asked if they had any comments on the experiment, if anything could be improved, if
anything was unclear, if the tariffs were understood, if they found it difficult to choose from the
7 point Likert scale, if a 4 point scale would not be better and again if they would like to add
anything.
The participants’ answers were consistent with one another, with the exception of one
participant, who did not have consistent views with the other participants, did not know English
very well and needed 30 minutes to finish the experiment. This points at the importance of
choosing respondents with sufficient knowledge of English. Following this incident, a Dutch
certified translation of the BIS/BAS questions, which had the highest level of English, was also
added to the experiment. The respondents mentioned that the experiment takes on average 20
minutes. This time of 20 minutes was added to the experiment in the welcome message in
order to let the respondents know how much the experiment would take and in order to
manage the respondent’s expectations. Moreover, two grammar mistakes were mentioned
that were modified immediately. For some respondents, the difference between the RTP and
TOU tariff was a bit confusing and therefore additional information was provided to make the
difference more clear and also an additional short explanation of all tariffs was added together
with a graph that showed an example of all six tariffs.
4.3 Controlling the noise
The experiment was conducted in such a way that the internal validity would be high. Among
others, the scale reliability technique (Hair et al., 2010a) was used in order to make sure that
the internal validity was high. The technique used different measurements for the same
concept. In order to minimize the measurement error (Hair et al., 2010b), twenty-four
questions were used to measure the risk-taking attitude, six questions were used to measure
34
the attitude towards renewable energy and five questions were used to measure tariff
selection. The tariff selection concept in relation to different types of framings was analyzed
from different perspectives/analyses (See section Analyses) and the output from each analysis
was consistent to the output from the other analyses. Moreover, the six cases/treatments of
the three experiments were randomly distributed to a large enough (Hair et al., 2010b) total
sample of 300 respondents (50 respondents per case). Randomization was used due to the fact
that it reduces unexpected influences by equalizing the external factors (“noise”) that have not
been taken into consideration in the experimental design (Fisher, 1926).
4.4 Analyses
In order to analyze thoroughly what influences people to choose for a particular tariff and what
an energy supplier should tell the users in order to promote the green real time pricing tariff, a
meta-analysis is of utmost necessity. The meta-analysis in this study combines the results of
several analyses that address the hypotheses listed in the Conceptual Model section. Below it
will be explained how this meta-analysis is constructed and what each additional analyses add
to the basic framing analysis that is derived from literature on framing.
The dependent variable of the paper, namely the tariff selection, was measured in multiple
ways in order to be analyzed from three perspectives: based on basic framing analysis, based
on Conjoint Analysis and based on Categorical Principal Component Analysis (CATPCA).
Additionally, the basic framing analysis based on the literature about framing took into account
multiple possible moderation variables (i.e. attitude towards renewable energy, risk taking
attitude - measured in terms of the overall BIS/BAS measure). Their moderating variables and
their respective hypotheses were explained more in depth under the Conceptual Model
section.
These three analyses provided in-depth information about the decision making of an individual
regarding energy tariffs. The last two analyses come as a support and important addition to the
research questions. Without the last two analyses, the conducted research would not be
complete. As mentioned before, the first analysis, basic framing analysis, is supported by
various framing literature and looks at the impact of positive and negative frames on the tariff
35
selection under each of the three types of identified frames (risky choice framing, attribute
framing and goal framing). The analysis was based on the questions for the three different
framings that were found in the literature; see Table 4 in the section Methodology – Content of
the Frames. The research goes further to look at whether the attitude towards renewable
energy would strengthen this relationship. This is important since it was proven before that the
attitude towards renewable energy does have an impact on tariff selection (Popov, 2012), and
now we should look at whether this concept strengthens even more the relationship between
framing and tariff selection. The research goes even further and looks at another concept, risk
taking attitude, and depicts whether risk taking attitude does strengthen the relationship
between the frames and tariff selection. The tariffs presented to the consumers do have
different degrees of risk and it may be that the tendency of people to choose for riskier tariffs is
influenced by the fact that they themselves are more risk taking (or risk averse).
The basic analysis compares the impact of positive framing with the impact of negative framing
(under different frames) on tariff selection and looks at some moderating variables that may
strengthen this relationship. Next to this basic analysis, the conjoint analysis adds a significant
contribution. It explains not only the impact of framing on a tariff, but also the impact of
framing on the weight that people put on different attributes of a tariff. The six tariffs that were
presented to the people had two attributes: degree of greenness (green tariff – 100%
renewable energy or grey tariff – 9% renewable energy) and degree of risk (flat tariff (low risk),
time of use tariff (moderate risk) and real time pricing (high risk)). More specifically, the
conjoint analysis looks at the weights to individual tariff components (degree of greenness or
degree of risk) by depicting whether the frames impact the perception of people about how
important degree of greenness and degree of risk of a tariff is for them. The conjoint analysis
answers the question: how are the attributes weighted and how is their weight affected by
framing?
The third analysis from the meta-analysis, the Categorical Principal Component Analysis
(CATPCA) compares not only the impact of a positive frame to the impact of a negative frame
within each of the three framings (risky choice, attribute, goal) like the first analysis, but
36
compares all six frames with each other. Unlike the basic analysis that is based on the framing
literature and has three different questions (preference, evaluation and choice) for the three
framings, the Categorical Principal Component Analysis (CATPCA) has the same consistent
question for all six cases of the experiment (positive and negative; risky choice, attribute and
goal). This consistency of the questions makes it possible to compare all the six frames with
each other.
4.4.1 Basic Framing Analysis
The tariff selection is defined in this paper as the process in which an individual is documenting
about different energy tariffs and is taking a decision regarding which energy tariff to prefer,
how to evaluate a tariff or what tariff to choose. As discussed in the Conceptual Model section,
all three dependent variables (preference for a tariff, evaluation of a tariff and choice of a tariff)
can be integrated into one variable: the tariff selection. However, (1) in the risky choice framing
context, the preference for a tariff variable was used, (2) in the attribute framing context, the
evaluation of a tariff was used and (3) in the goal framing context, the choice of a tariff was
used. The reason for different dependent variables is the fact that all three were measured
differently according to the literature in which the three types of frames are discussed.
Nevertheless, all three variables eventually fall under the umbrella of the tariff selection, which
had different measurements as well (discussed under the section Conjoint Analysis and the
section Categorical Principal Component Analysis).
Different measurements were chosen so that the internal validity of the experiment would be
high. The technique that uses these different measurements is named scale reliability and is
one of the two techniques that are widely used in order to increase internal validity (Hair et al.,
2010a).
The measurements of the preference for a tariff, evaluation of a tariff and choice of a tariff
were done with the help of two questions that were used in different framing literature. The
first question was the measurement of the dependent concepts and the second question had
the role of checking whether the respondents were sure of their answers. These questions can
be found in Table 4. While for the measurement of the preference for a tariff from the risky
37
choice framing, participants were asked to think about which tariffs they would prefer; for the
measurement of the evaluation of a tariff (i.e. attribute framing) participants were asked to
evaluate the green real time pricing tariff; and for the measurement of the choice of a tariff,
participants were asked to what extent they would agree to choose the green real time pricing
tariff.
Table 5 shows the measurement of the tariff selection. The answer that the respondent gives to
the frame specific questions is Yn.n. Through a Cross Tables Analysis & T-test Analysis (Cross
Tables for experiment 1 with ordinal variables and t-test for experiment 2 and 3 with scale
variables) and an Univariate Analysis a Variance (ANOVA), it is checked whether the answer
that is given to the question from the positive frame is significantly different from the answer
that is given to the question from the negative frame. This will be done in order to check the
hypotheses one, two and three.
Table 5: Measurement of the Tariff Selection
Cases group 1
Tariff
Cases group 2
Tariff Selection
Selection
Case 1.1.Risky Choice Framing +
Y1.1
Case 2.1.Risky Choice Framing -
Y2.1
Case 1.2. Attribute Framing +
Y1.2
Case 2.2.Attribute Framing -
Y2.2
Case 1.3. Goal Framing +
Y1.3
Case 2.3. Goal Framing -
Y2.3
Next to the Cross Tables Analysis, T-test Analysis and Univariate Analysis of Variance of the
direct relationship between the framing and tariff selection, the same analyses will be applied
on impact of different moderating variables on the initial relationship: attitude towards
renewable energy and risk taking attitude, measured on the basis of the overall BIS/BAS
measure.
For the first experiment, the Cross Tables Analysis was used since the preference for a tariff
variable is a nominal variable with six different types of tariffs. For the second and third
experiment, a T-test Analysis was conducted since the evaluation of a tariff and choice of a
tariff variables are ordinal variables. The reanalyzed relation after controlling for the
38
moderating variables ‘attitude towards renewable energy’ and ‘risk taking attitude’ was based
on the median split. The relation was reanalyzed based on ANOVA with a covariate.
Univariate Analysis of Variance was conducted next to the Cross Tables and T-test Analysis. The
ANOVA with a covariate analysis was done as well because the analysis based on the median
split received the criticism that this first analysis may often lead misleading results (MacCallum
et al., 2002). However, the results from ANOVA are consistent with the results from the
previous analysis and in our case the analysis based on median split did not show misleading
results.
4.4.2 Conjoint Analysis
Conjoint Analysis portrays consumer’s decisions as tradeoffs among multi-attribute products or
services (Hubber 1987). It was chosen as an analysis method for the experiment since it
provides substantial insight about the individual preferences while maintaining a high degree of
realism (Hair et al., 2010b).
The Conjoint Analysis was only a part of the overall analysis of tariff selection. For the Conjoint
Experiment, respondents were asked to rank several tariffs in the order of preference: from 1
for the most preferred to 6 for the least preferred (for more information see section Structure
of the Experiment, paragraph Ordering of Tariffs and Expressed Preference). In order to
conduct a conjoint experiment, a minimum sample size of 50 respondents per case was needed
(Hair et al., 2010b). That is why the analysis is applied to the total sample of 307 respondents.
Table 6: Attributes Values
Attributed
Value
Attributes
Degree of Risk
Flat
Time of Use
Real Time Pricing
-1
0
1
Degree of Greenness
Green
Grey
-1
1
The Conjoint Analysis was based on the framing and the preference ordering question for the
six tariffs. The tariffs had two attributes: Degree of Risk (flat, TOU, RTP) and Degree of
39
Greenness (grey, green). Within the Degree of Risk attribute, the attributed value to the flat
tariffs was -1, to the time of use tariffs was 0 and to the real time pricing tariffs was 1. Within
the Degree of Greenness, the attributed value to grey tariffs was -1 and to green tariffs was 1;
see Table 6. The attributed values were given to the attributes on the basis of the assumption
that the distances between the outcomes from each attribute are equal.
4.4.3 Categorical Principal Component Analysis
The main goal of the Categorical Principal Analysis (CATPCA) is to reduce an original set of
concepts into a smaller set of uncorrelated components that exemplify the most information
from the original variables (IBM, 2011). A set of variables is analyzed to reveal major
dimensions of variation (Meulman et al., 2001). For this analysis, the respondents were asked
give a number from 1 to 100 to each of the six numbers in terms of preference (for more
information see section Structure of the Experiment, paragraph Ordering of Tariffs and
Expressed Preference). The output of the analysis showed how close the six tariffs are to each
other and how their proximity is influenced by different frames.
40
5 Results
The results of the four mentioned analyses (Cross Tables & T-Test Analysis, Univariate Analysis
of Variance, Conjoint Analysis and Categorical Principal Component Analysis) can be found in
the following sections. In Appendix 2, the means, standard deviations together with their pvalues as well as the conclusion of all three experiments can be found.
5.1 Descriptive Analysis of the Experiments
Table 7 shows the descriptive results of the three analyses. The table also summarizes the
results of the following analysis and shows whether the relationships between each of the
frames and tariff selection are significant.
5.1.1 Experiment 1: Risky Choice Framing
One hundred and one students from a Dutch university (62 men and 39 women, M age = 22.15
years, SD = 3.36) were randomly assigned to the conditions of a risky choice frame
experimental design, to which individual attitude towards renewable energy (Bang et al., 2000)
and risk taking attitude (Carver & White, 1994) were added as covariates.
5.1.2 Experiment 2: Attribute Framing
Ninety nine students from a Dutch university (63 men and 36 women, M age = 22.82 years, SD =
4.40) were randomly assigned to the conditions of a (positive, negative) attribute frame
experimental design. Individual attitude towards renewable energy and risk taking attitude
were added as covariates.
5.1.3 Experiment 3: Goal Framing
One hundred and seven students from a Dutch university (60 men and 47 women, M age =
23.59 years, SD = 5.28) were randomly assigned to the conditions of a (positive, negative) goal
frame experimental design. Once more, individual attitude towards renewable energy and risk
taking attitude were added as covariates.
41
Table 7: Descriptive Results
Positive Frame
Standard
Question
Mean / Count
Deviation /
Percentage
Negative Frame
Mean /
Count
Standard
Significant
Supported
Deviation /
difference?
/Rejected
Percentage
Which of the tariffs would you favor?
(1) Grey flat tariff
3
7,0%
10
23,3%
(2) Green flat tariff
9
20,9%
7
16,3%
Choice
(3) Grey time of use tariff
5
11,6%
2
4,7%
Framing
(4) Green time of use tariff
16
37,2%
9
20,9%
(5) Grey real time pricing tariff
2
4,7%
3
7,0%
(6) Green real time pricing tariff
9
18,6%
12
27,9%
Risky
Attribute
Framing
Goal
Framing
How would you evaluate this green
real time pricing tariff?
(1) Very positively
(2) Mostly positively
(3) Somewhat positively
(4) Neither positively nor negatively
(5) Somewhat negatively
(6) Mostly negatively
(7) Very negatively
After you have been reading this, will
you choose the green real time pricing
tariff?
(1) Strongly agree
(2) Agree
(3) Somewhat agree
(4) Neither agree nor disagree
(5) Somewhat disagree
(6) Disagree
(7) Strongly disagree
No
χ25 =9,255 p=0.160
Not
supported
Yes
2,239
1,239
4,163
1,632
Ts =-5,930, p<0.001
Supported
F=35,899, p=0,000
No
3,2292
1,627
3.568
1,797
Tp =-0,950, p=0,345
F=0,902, p=0,345
Not
supported
42
5.2 Impact of Framing on Tariff Selection
The impact of framing on tariff selection was measured based on Cross Tables Analysis, T-test
and Univariate Analysis of Variance. The results of this section represent the results from the
basic analysis of the relationship between framing and tariff selection and the covariates
attitude towards renewable energy and risk taking attitude.
5.2.1 Experiment 1: Risky Choice Framing
Table 8 summarizes the preferred tariffs under the two framing scenario’s. It shows that green
tariffs were usually preferred over the parallel gray tariffs, and that real time pricing tariffs were
overall the least preferred. Expressed tariff preferences differed for the two framing conditions.
For negative risky choice, the green real time pricing tariff was selected relatively frequently,
whereas for positive risky choice, the green time of use and real time pricing tariffs were
selected relatively frequently. The differences were not sufficiently large to reject assumed
independence of preferred tariff and framing type (χ25 = 9,255, p = 0,160).
Table 8: Preferred Tariff under Risky Choice Framing
Tariff
1. Gray Flat
2. Green Flat
3. Gray TOU
4. Green TOU
5. Gray RTP
6. Green RTP
Total
Total
Count
Percentage
13
14,9%
16
18,4%
7
8,0%
25
28,7%
5
5,7%
21
24,1%
87
100%
Risky Choice Framing
Positive
Negative
Count
Percentage
Count
Percentage
3
7,0%
10
23,3%
9
20,9%
7
16,3%
5
11,6%
2
4,7%
16
37,2%
9
20,9%
2
4,7%
3
7,0%
9
18,6%
12
27,9%
44
100%
43
100%
Further, we reanalyzed this relation after controlling for attitude towards renewable energy
based on median split. The latter has been applied for convenience, in spite of criticisms against
dichotomization of continuous variables (Maxwell & Delaney, 1993; MacCallum et al., 2002).
For people with a relatively negative attitude towards renewable energy, we found a slight
over-representation of preferences for the grey flat tariff under negative risky choice frame,
and a marginal over-representation of all gray tariffs under positive risky choice frame. There
43
was no sufficient evidence in favor of an assumed dependency between tariff preference and
risky choice frame (χ25 = 5,062, p = 0,4083; Fisher’s p = 0,4225).
For people with a relatively positive attitude towards renewable energy, we found an overrepresentation of green real time pricing tariffs and green time of use tariffs under the negative
risky choice frame, and over-representation of green time of use and real time tariffs under the
positive risky choice frame. Again, the relationship between tariff choice and framing was not
significant (χ25 = 5,579,p = 0,349; Fisher’s p = 0,4137).
Moreover, for people with a relatively low risk taking attitude, there was an overrepresentation again of green time of use tariff under the positive risky choice framing and an
over-representation of green real time pricing tariff under the negative risky choice framing.
The relationship between tariff choice and framing was not significant (χ25 = 4,082,p = 0,666).
For people with a relatively high risk taking attitude, there was an over-representation of green
time of use tariffs under the positive frame and over-representation of grey flat tariff and green
time of use tariff under the negative risky choice frame. The relationship between tariff choice
and framing was significant (χ25 = 11,173,p = 0,048).
This finding does not offer confirmation for our Hypothesis 1 that people prefer riskier energy
tariffs under a negative risky choice frame, but surer energy tariffs under a positive risky choice
frame. Neither do the findings confirm Hypothesis 4 that takes into consideration the attitude
towards renewable energy moderating variable. However, Hypothesis 7 is confirmed when the
analysis is controlled for risk taking attitude.
5.2.2 Experiment 2: Attribute Framing
Table 9 summarizes the extent to which respondents would evaluate positively the risky choice
tariff. Results indicate that the mean preference score under positive attribute framing
(M = 2,239, SD = 1,239) was significantly lower than under negative attribute framing
(M = 4,163, SD = 1,632); mean difference −1,822, TS = −5,930, p < 0.001. Thus, people preferred
a positively attributed green real time pricing tariff over a negatively attributed one.
44
Table 9: Green Real Time Pricing under Attribute Framing
Green
Real Time Pricing
No mediating variable
Low attitude towards
renewable energy
High attitude towards
renewable energy
Low risk taking
attitude
High risk taking
attitude
Positive
Mean
Standard
Deviation
2,239
1,239
Attribute Framing
Negative
Difference – T-test
Mean
Standard
Mean
TS / TP
Deviation
4,163
1,632
-1,822
-5,930(TS)
p<
0,001
2,773
1,378
4,600
1,729
-1,827
-3,800(TP)
0,001
1,960
0,978
3,783
1,476
-1,823
-5,000(TS)
0,001
2,3750
1,439
3,947
1,779
-1,572
-3,206 (TP)
0,001
2,158
1,015
4,333
1,523
-2,175
-5,351 (TP)
0,001
Interestingly, after controlling for attitude for renewable energy based on median split, we
found similar results. For people with a relatively negative attitude towards renewable energy,
the mean preference score under positive attribute framing (M = 2,773, SD = 1,378) was
significantly lower than that under negative attribute framing (M = 4,600, SD = 1,729); mean
difference −1,827, TP = −3,800, p < 0.001.
For people high on attitude towards renewable energy, also their mean preference score under
positive attribute framing (M = 1,960, SD = 0,978) was significantly lower than under negative
attribute framing (M = 3,783, SD = 1,476); mean difference −1,823, TS = −5,000, p < 0.001. The
same analysis was made for controlling for the risk taking attitude, but no significant results
were found. This offered confirmation for our Hypothesis 2, and also indicated that attribute
framing is effective regardless someone’s attitude towards renewable energy.
After controlling for risk taking attitude based on median split, we found that people with a
relative low risk taking attitude, the mean preference score under positive attribute framing
(M = 2,3750, SD = 1,439) was significantly lower than under negative attribute framing
(M = 3,947, SD = 1,779); mean difference −1,1572, TP = −3,206, p < 0.001. The results were
similar for people with high risk taking attitude, where the mean preference score under
positive attribute framing (M = 2,158, SD = 1,015) was significantly lower than under negative
attribute framing (M = 4,333, SD = 1,523); mean difference −2,175, TP = −5,351, p < 0.001.
45
Univariate Analysis of Variance with and without covariates was conducted for the second
experiment; see Table 10. The analysis showed that for the attribute framing, there is a
significant relationship (F = 35,899, p = 0.000, R Squared = 0,299) between attribute framing
(positive/negative) and the extent to which the respondents evaluated positively the green real
time pricing tariff. The attitude towards renewable energy (F = 10,152, p = 0,002) also had an
impact on the relationship (F 24,981, p = 0.000, R Squared = 0,361).
Table 10: Univariate Analysis of Variance: Experiment 2
Model 1a
F
p
35,899
0,000
Model 2b
F
p
24,981
0,000
Model 3c
F
p
17,736
0,000
Model 4d
F
p
16,569
0,000
Framing  Tariff Preference
Framing  Tariff Preference
Attitude towards renewable
10,152
0,002
10,272
0,002
energy
Framing  Tariff Preference
0,000
0,997
0,216
0,643
Risk taking attitude
2
R
0,299
0,376
0,299
0,377
a Model 1 – ANOVA without covariate
b Model 2 – ANOVA with attitude towards renewable energy as a covariate
c Model 3 – ANOVA with risk taking attitude as a covariate
d Model 4 – ANOVA with attitude towards renewable energy and risk taking attitude as covariates
The risk seeking variable (F = 0,000, p = 0,997) did not have an impact on the relationship
(F 17,736, p = 0.000, R Squared = 0,299). These results confirm the Hypothesis 2 and Hypothesis
5 as in the previous T-test, but does not confirm the Hypothesis 8 that considers risk taking
attitude as a moderate variable.
5.2.3 Experiment 3: Goal Framing
Table 11 summarizes the extent to which respondents would evaluate positively the risky
choice tariff. No significant difference was found between the mean preference scores for
positive and negative goal framing. The mean preference score for the positive goal frame
(M = 3,229, SD = 1,627) was close to that of the negative goal frame (M = 3,568, SD = 1,797);
mean difference −0,339, TP = −0,950, p = 0.345.
Controlling for someone’s attitude towards renewable energy did alter this result. For people
with a relatively negative attitude towards renewable energy, the mean preference score under
positive goal framing (M = 3,333, SD = 1,617) was slightly significantly lower than that under
negative goal framing (M = 4,155, SD = 1,558); mean difference −0,782, TP = −1,790, p = 0,079.
46
This provides mild support for Hypothesis 6. People with relatively positive attitude towards
renewable energy, had a mean preference score under positive goal framing (M = 3,095,
SD = 1,671) slightly higher than that under negative goal framing (M = 2,778, SD = 1,865); mean
difference 0,317, TP = 0,560, p = 0.578.
Table 11: Green Real Time Pricing under Goal Framing
Green
Real Time Pricing
No mediating variable
Low attitude towards
renewable energy
High attitude towards
renewable energy
Low risk taking
attitude
High risk taking
attitude
Positive
Mean
Standard
Deviation
3,229
1,627
Goal Framing
Negative
Difference – T-test
Mean
Standard
Mean
TP
Deviation
3,568
1,797
-0,339
-0,950
0,345
3,333
1,617
4,155
1,558
-0,782
-1,790
0,079
3,095
1,671
2,778
1,865
0,317
0,560
0,578
3,429
1,502
3,920
1,778
0,491
-1,001
0,322
3,074
1,730
3,105
1,761
-0,031
-0,060
0,953
P
People with a low risk taking attitude had a mean preference score under positive goal framing
(M = 3,429, SD = 1,502) slightly lower than that under negative goal framing (M = 3,920,
SD = 1,778); mean difference 0,491, TP = -1,001, p = 0.322, and people with a high risk taking
attitude also had a mean preference score under positive goal framing (M = 3,074, SD = 1,730)
slightly lower than that under negative goal framing (M = 3,105, SD = 1,761); mean difference
-0,031, TP = -0,060, p = 0.954. However, controlling for the risk taking attitude did not change
the previous findings.
The results were inconsistent with our Hypothesis 3 that people, once exposed to a negative
goal frame, would more often select the green real time pricing tariff than after exposure to a
positive goal frame. The same outcomes for the framing effect are obtained when controlling
for renewable energy attitude and risk taking attitude. Thus our Hypothesis 6 and Hypothesis 9
were not confirmed.
Table 12 shows the results based on the Univariate Analysis of Variance without and with
covariates. No significant relationship was found (F = 0,902, p = 0,345, R Squared 0,010)
47
between the goal framing and tariff selection in the third experiment. Here the goal framing’s
influence on the choice of green real time pricing tariff was tested.
Table 12 Univariate Analysis of Variance: Experiment 3
Model 1a
F
p
0,902
0,345
Model 2b
F
p
1,824
0,165
Model 3c
F
p
0,772
0,465
Model 4d
F
p
1,285
0,284
Framing  Tariff Preference
Framing  Tariff Preference
Attitude towards renewable
2,764
0,100
2,290
0,134
energy
Framing  Tariff Preference
0,645
0,424
0,205
0,652
Risk taking attitude
2
R
0,010
0,040
0,017
0,042
a Model 1 – ANOVA without covariate
b Model 2 – ANOVA with attitude towards renewable energy as a covariate
c Model 3 – ANOVA with risk taking attitude as a covariate
d Model 4 – ANOVA with attitude towards renewable energy and risk taking attitude as covariates
When the attitude towards renewable energy was taken into consideration, the significance
was changed but not enough to make the relationship between goal framing and tariff selection
significant (F = 1,842, p = 0,165, R Squared = 0,040). Moreover, the attitude towards renewable
energy was found to have a slight significant influence on the relationship between goal
framing and tariff selection (F = 2,764, p = 0,100). As for the risk seeking variable (F = 0,645,
p = 0424), no significant influence was found on the relationship between goal framing and
tariff choice (F = 0,772, p = 0,456, R Squared = 0,017).
5.3 Analysis of Individual Preferences for Tariff Attributes
Table 13 shows the results of the Conjoint Analysis. The preference question (“Please specify
how likely it is to choose each of the tariffs by RANKING them in the table below” – from 1 to 6)
was asked two times: once before the frame with the additional information was shown
(pretest) and another time after the frame was shown (posttest). The average relative weight
for the pretest was higher for the Degree of Risk attribute (part worth utility 0,74 relative
weight 55%) than for the Degree of Greenness attribute (part worth utility 0,58, relative weight
45%). This means that in the pretesting phase, the respondents concentrated more on the
degree of risk than on the degree of greenness of a tariff. This finding is consistent with the
findings from the posttest (post-variables from all six frames) where in all instances the
48
respondents were inclined to put more emphasis on the Degree of Risk than on the Degree of
Greenness.
Table 13: Part Worth Utilities and Relative Weights
Part Worth Utilities (Relative Weights)*
Risky Choice Framing
Degree of Risk
Positive
Negative
Total
Attribute Framing
Positive
Negative
Total
Goal Framing
Positive
Negative
All frames together
Total
Positive
Negative
Total
Posttest – Pretest
Regression
Difference Significance
Significance
Degree of Greenness
Risk
Greenness
F & p-value
Pretest
Posttest
Pretest
Posttest
T&p
T&p
Pretest
Posttest
0,69
0,75
0,61
0,51
2,032
-1,8444
33,925
17,486
(52%)
(57%)
(48%)
(43%)
p=0,048
p=0,072
p=0,000
p=0,000
0,64
0,70
0,61
0,49
0,907
-2,020
25,285
6,466
(50%)
(55%)
(50%)
(45%)
p=0,370
p=0,050
p=0,000
p=0,002
0,70
0,72
0,60
0,50
2,043
-2,683
57,820
22,260
(53%)
(56%)
(47%)
(44%)
p=0,044
p=0,009
p=0,000
p=0,000
0,71
0,73
0,60
0,62
4,809
0,688
29,687
57,766
(54%)
(51%)
(46%)
(49%)
p=0,000
p=0,496
p=0,000
p=0,000
0,76
0,81
0,57
0,52
0,937
-0,276
13,619
13,800
(58%)
(61%)
(42%)
(39%)
p=0,354
p=0,784
p=0,000
p=0,000
0,78
0,77
0,57
0,57
-2,841
0,361
41,845
48,967
(58%)
(56%)
(42%)
(44%)
p=0,006
p=0,719
p=0,000
p=0,000
0,81
0,87
0,55
0,53
2,859
0.000
16,240
29,648
(60%)
(61%)
(40%)
(39%)
p=0,006
p=1.000
p=0,000
p=0,000
0,81
0,80
0,56
0,58
-1,524
0,965
36,593
26,549
(58%)
(56%)
(42%)
(44%)
p=0,135
p=0,340
p=0,000
p=0,000
0,74
0,83
0,57
0,55
3,086
-0,621
22,148
51,917
(55%)
(59%)
(45%)
(41%)
p=0,003
p=0,536
p=0,000
p=0,000
0,76
0,78
0,56
0,55
2,368
-0,400
76,029
79,158
(57%)
(57%)
(43%)
(43%)
p=0,019
p=0,690
p=0,000
p=0,000
0,72
0,77
0,59
0,53
0,075
-1,192
60,237
38,979
(54%)
(57%)
(46%)
(43%)
0 p=,940
p=0,235
p=0,000
p=0,000
0,74
0,79
0,58
0,55
1,645
-1,150
133,755
104,940
(55%)
(57%)
(45%)
(43%)
p=0,101
p=0,251
p=0,000
p=0,000
*The numbers that are in brackets represent the respective relative weights.
49
In fact in all instances, both in the pretest (F = 133,755, p = 0,000) and the posttest (F = 104,940,
p = 0,000) the respondents put more emphasis on the Degree of Risk than on the Degree of
Greenness. That shows that regardless of the six different frames administered, the people will
find risk more important than greenness in the context of selecting an energy tariff.
What can also be seen from the output of the Conjoint Analysis is that almost in all cases,
respondents considered that the risk of a tariff was more important than its greenness in the
post-testing than in the pre-testing. The exceptions were in the positive attribute framing
(pretest relative weight 54%, F = 29,687, p = 0,000; posttest relative weight 51%, F = 57,766,
p = 0,000) and in the negative goal framing (pretest relative weight 58%, F = 36,593, p = 0,000;
posttest relative weight 56%, F = 26,549, p = 0,000).
The respondents concentrated the most on the degree of risk compared to the degree of
greenness under the goal positive frame (post-test relative weight 61%, part worth utility 0,87,
F = 29,648, p = 0,000) and under the attribute negative frame (post-test relative weight 61%,
part worth utility 0.81, F = 13,800, p = 0,000) and they concentrated the least on the degree of
risk under the post-testing of the positive attribute frame (relative weight 51%, part worth
utility 0,73, F = 57,766, p = 0,000) but also under de pre-testing of the risky choice negative
frame (relative weight 50%, part worth utility 0,64, F = 25,285, p = 0,000).
In all instances the part worth utilities for the degree of risk were positive, which means that
respondents put more emphasis on and choose riskier tariffs. This is based on the fact that the
real time pricing tariffs had an attributed value of 1 in the Conjoint Analysis and the flat tariffs
had an attributed value of -1; see Table 6 under the Methodology section. Also all the part
worth utilities for the degree or greenness were positive, meaning that the respondents put
more emphasis on and choose greener tariffs. This is based on the fact that green tariffs had an
attributed value of 1 and the grey tariffs had an attributed value of 1; see Table 6 under the
Methodology section.
Finally, in almost all instances, the part worth utility of the respondents for the degree of risk
from the pre-testing phase increased in the post-testing phase, with the exception of the
negative goal frame where it decreased (pretest part worth utility 0,81, posttest part worth
50
utility 0,80). The posttest part worth was significantly higher than the pretest in the case of
positive risky choice frame(T = 2,032, p = 0,048), total risky choice frame (T = 2,043, p = 0,044),
positive attribute frame (T = 4,809, p = 0,00), total attribute frame (T = 2,841, p = 0,006),
positive goal frame (T = 2,859, p = 0,006) and total goal frame (T = 3,086, p = 0,003). This means
that the positive risky choice frame, positive attribute frame and positive goal frame
significantly influenced the respondents to choose for riskier tariffs. On the other hand, almost
in all instances, the part worth utility of the degree of greenness from the pretest phase
decreased in the posttest phase, with the exception of the positive attribute frame (pre-testing
part worth utility 0,60; post-testing part worth utility 0,62) and negative goal frame (pretest
part worth utility 0,56; posttest part worth utility 0,58) where it increased. However, this
decrease was significant only in the case of positive risky choice (T = -1,8444, p = 0,072),
negative risky choice (T = -2,020, p = 0,050) and total risky choice (T = -2,683, p = 0,009). This
means that the positive and negative risky choice frames significantly influenced the
respondent to choose for greyer tariffs.
5.4 Analysis of Tariff Proximity under Different Framing
Table 14 to 17 show the output of Categorical Principal Component Analysis. While this analysis
explains tariff proximity in terms of preference scores of the individuals, the six vectors from
the biplots represent the six tariffs.
Table 14: CATPCA Output for All Frames Together
All Frames Together
Posttest
Total
Pretest
51
Table 14 shows the CATPCA graphic output of the tariff preferences for the pretest and the
posttest of, which is based on all observations.
Table 15: CATPCA Output for the Risky Choice Framing
Risky Choice Framing
Posttest
Total
Negative
Positive
Pretest
52
We can see that in the pretest, see Table 14, the distribution of the tariff vectors is as expected.
The real time pricing tariffs are separated from the time of use tariffs which are separated from
the flat tariffs. The distance from each of these tariffs (flat, TOU and RTP) is approximately the
same. However, the respective green and grey tariffs from each type of tariff (flat, TOU, RTP)
are close to each other. This shows again like in the conjoint analysis that people put more
emphasis on the distinction of tariff based on risk (i.e. flat, TOU, RTP) than based on greenness
(i.e. grey, green). Nevertheless, in the posttest, the distance between the green and grey tariffs
is increasing.
To examine the effect of framing on the factor structure, we split the sample according to the
six framings and performed a CATPCA in each of the subgroups (Peralta & Cuesta, 2005). The
CATPCA output of the tariff preferences result for the risky choice framing experiment 1 can be
found in Table 15, that of the attribute framing experiment 2 in Table 16 and that of the goal
framing experiment 3 in Table 17. For each of the experiments the analysis was conducted both
before and after the treatment was administered.
5.4.1 Global View of the Categorical Principal Components Analysis
The six tariffs resulted in two dimensions with eigenvalue almost always higher than 1. An
exception occurred for the posttest of the positive risky choice frame, where the second
highest eigenvalue was close to 1. The fulfillment of this criterion showed that reducing our six
tariffs to only two dimensions was enough and no extra dimensions needed to be added. This
means that the output from each of the groups/subgroups of the performed Categorical
Principal Component Analysis is valid and can be taken into consideration (Meulman et al.,
2001).
In the case of the two dimensions, they together explained 64,63% of the variance from the
post test of the entire sample, where Dimension 1 explained 37.75% of the variance and
Dimension 2 explained 26,88% of the variance. In the pretest, however, the two dimensions
explained 70,33% of the variance in the six tariffs, where Dimension 1 explained 46,08% of the
variance and Dimension 2 explained 24,25% of the variance in the six types of tariffs.
53
Table 16: CATPCA Output for the Attribute Framing
Attribute Framing
Posttest
Total
Negative
Positive
Pretest
According to the subjective procedure discussed by Hair (2010b), the two dimensions of the
graphs below can be interpreted in terms of the attributes of the six tariffs. Therefore, to
54
interpret the biplots, we will “look for directions through the plot that show a continuous
change in some attribute of the tariffs or [we will] look for regions in the plot that contain
clusters of tariff points and determine what the [tariffs] have in common” (SAS Institute, 2012,
p. 5).
5.4.2 Explanation of Dimension 2
In almost all the biplots from Tables 14 to 17 (with the exception of the pretest for the data
from positive risky choice framing), the RTP tariffs are located on top of the graph (in some
instances on the bottom), the TOU tariffs are located in the middle and flat tariffs are located
on the bottom of the graph (in some instances on the top). The second principal component
(Dimension 2) differentiates RTP tariffs from TOU tariffs and from flat tariffs and thus
represents the degree of risk of the tariffs. In the case of the mentioned pretest for the positive
risky choice framing, Dimension 2 represents the degree of greenness, since all the green tariffs
are on top of the space and all the grey tariffs are on the bottom of the space.
5.4.3 Explanation of Dimension 1
It is easier to spot what Dimension 2 represents than Dimension 1, where the differences in
scale are very small. The weakness of the Dimension 1 can be seen also from the small
differences between tariffs from the x-axis in Tables 14 to 17.
Dimension 1 separates the RTP tariffs from the other tariffs (TOU tariffs and flat tariffs) and
make them stand out from the other tariffs in the case of negative risky choice framing (both
pretest and posttest), pretest of the risky choice framing, positive attribute framing (both
pretest and posttest), posttest of the negative attribute framing, attribute framing (both
pretest and posttest) and pretest of the negative goal framing. Also Dimension 2 separates the
flat tariffs from the other tariffs and makes them stand out in the case of the pretest of the
global analysis (all frames together), posttest of the positive risky choice framing, posttest of
the risky choice framing and pretest of the goal framing. Dimension 2 also separates the time of
use tariffs from the other tariffs in the case of the posttest goal framing (positive and negative
and total). In the case of the pretest of the negative attribute framing and the pretest of the
55
risky choice framing, Dimension 2 represents the degree of greenness and in the rest of the
cases that were not mentioned, Dimension 2 gives inconclusive findings.
Table 17: CATPCA Output for the Goal Framing
Goal Framing
Posttest
Total
Negative
Positive
Pretest
56
Thus, Dimension 2 usually makes one of the types of tariffs (real time pricing tariff, flat tariff
and time of use) stand out and be differentiated from the other tariffs. Rarely does it happen
that either Dimension 1 or Dimension 2 represent the degree of greenness, which shows once
again that the respondents are more focused on the degree of risk than on the degree or
greenness.
5.4.4 Tariff Proximity
As the goal of this paper is to find out how to convince people to switch from the traditional
grey flat tariff to the more effective green real time pricing, it is important to look for the frame
that shows the smallest distance between the grey flat vector and green real time pricing. The
distance between the two vectors will show how close people perceive the two tariffs to be,
and thus how easily they would switch from the traditional tariff to the modern one. The
distance between the two vectors can be measured as the angle (gamma) between the grey flat
vector and the green real time pricing vector. The calculated angles for each framing can be
found in Appendix 3.
In the positive(increased with 4,8⁰) as well as negative (increased with19,3⁰) risky choice
framing, the angle between the grey flat and green RTP vector becomes larger in the posttest
compared to the pretest, meaning that the frames that are attributed are not effective in
bringing the two vectors closer. However, it can also be observed that in the positive as well as
negative attribute framing (positive: decreased with 5,0⁰; negative: decreased with 29,5⁰) and
goal framing (positive: decreased with 20,0⁰; negative: decreased with 6,2⁰), the angle between
the grey flat and green RTP becomes smaller in the posttest compared to the pretest, meaning
that the positive and negative frames from both attribute and goal framings are effective in
bringing the two tariffs closer. The largest difference between the posttest angle and the
pretest angle occurs in the negative attribute framing (posttest: 106,0⁰; pretest: 86,6⁰). While
the difference in angle between the posttest and pretest occurs in the negative attribute frame,
the smallest angle in the posttest phase is still found under the same framing (86,6⁰). However,
the angle found in the posttest from the positive attribute framing (89,4⁰)is also close to the
angle from the posttest from the negative attribute framing (86,6⁰), which means that also the
57
positive attribute framing is a promising tool for bringing the two tariffs closer to each other. It
seems therefore that the negative attribute frame is the most efficient in increasing (posttest –
pretest) the proximity between the two tariffs, regarding individual preference scores. Unlike
the prior analyses that strongly recommended the positive attribute framing, CATPCA primarily
recommended the negative attribute framing in bringing closer the individual preference scores
for the grey flat tariff and green real time pricing tariff. This difference in results can be
attributed to the fact that the graphs from CATPCA explained only 64,63% of the variance and
not the entire variance, while in the other analysis all variance was taken into consideration.
58
6 General Discussion and Conclusion
The paper researched the issue of how to convince energy consumers to switch from traditional
to dynamic and renewable energy pricing. Building on behavioral economics research (Tversky
& Kahneman, 2003), we suggested that the “framing” of tariff-related messages in positive or
negative terms would influence consumers’ preferences towards dynamic energy pricing and
renewable energy to the extent that it would force them to accept or reject a tariff alternative
on valence-based, rather than rational grounds. Results from three behavioral experiments
confirmed that one of the explored framing effects generally impacts consumers’ energy tariff
selection, but that two of them do not. Also, we confirmed prior research suggesting that
individual differences in attitude towards renewable energy slightly impact energy-related
decision making under the goal framing (Bang et al., 2003). Moreover, we confirmed that
individual differences in risk taking attitude (Carver & White, 1994) impact energy-related
decision making under the risky choice framing, unlike prior research showed (Popov, 2012).
6.1 Theoretical Relevance
In the present research, we focused on three of the most commonly found framing effects: the
classic risky choice framing effect (Tversky & Kahneman, 2003) as well as attribute framing, and
goal framing (Levin et al., 1998). A meta-analysis was conducted and the results from each
analysis were consistent with the other conducted analyses, with the exception of the negative
attribute framing from the tariff proximity analysis. This difference in results can be attributed
to the fact that the output from the tariff proximity analysis was based on results that explained
only a part of the variance from all the aspects of tariff preference. Despite this deviation from
the general observation of the analyses, the positive attribute framing was confirmed to be the
most effective tool to convince people to choose for the green real time pricing tariff. Also the
results of the analyses emphasized that people are more focused on the riskiness of a tariff
than its greenness. In the next sections, the findings from the three separate experiments will
be discussed and then the conclusions from the meta-analysis will be addressed.
59
6.1.1
Discussion of the Separate Framing Experiments
To the best of our knowledge, this paper is the first research written concerning the impact of
framing on energy tariff selection. Unlike other research that considers only one type of
framing, the paper considered three types of framing (Levin et al., 1998) in order have a
broader range that would enable us to better advise energy companies about the best way to
advertise the green real time pricing tariff.
We confirmed prior research with respect to attribute framing (Davis & Bobko, 1986; Koriat et
al, 1980) as having a framing effect as well as with regards to risky choice framing (Fagley &
Cruger, 1986; Fragley & Miller, 1987) and goal framing (Lauver & Rubin, 1990; Lerman et al.,
1992), where we did not find a framing effect. We also reported opposite findings to other
research with regards to risky choice framing (Bohm & Lind, 1992; Maule 1989; Neale &
Bazerman, 1985), attribute framing (Sniezek et al., 1990) and goal framing (Loewenstein &
Issacharoff, 1994; Meszaros et al., 1991). Levin et al. (1998) argue that the seemingly
“contradictory” findings may be due to the differences in what is framed, what is affected and
how the framing effect is measured.
The findings of our research confirmed that attribute framing is the most effective tool in
convincing people to choose for the green real time pricing tariff. It was demonstrated that
people will evaluate better a positively presented green real time pricing tariff over a negatively
presented green real time pricing tariff given the factual information. However, the results did
not confirm the hypotheses that people will prefer riskier tariffs under a negative frame, but
surer, i.e. less risky, tariffs when under a positive choice frame. Neither did the findings show
that under a negative goal frame people, will choose the green real time pricing tariff more
often than under a positive goal frame. Nevertheless, what the paper adds to the current
research is that the impact of positive versus negative risky choice framing on tariff selection
does become significant for people with high risk taking attitude, and in the case of goal
framing, for people with a less positive attitude towards renewable energy. This will be
discussed in greater detail in the following paragraph.
60
6.1.2 Discussion of the Two Moderators in the Present Research
The findings also confirmed the research (Batley et al., 2001, Bergman et al., 2006, Brochers et
al., 2007) that argued that people are willing to pay for renewable energy, by showing that
people tend to choose green tariffs more often than grey tariffs. However, none of the frames
were effective in convincing people to choose more often for green energy (Tversky &
Kahneman, 2003) and people always put less emphasis on the degree of greenness than on the
degree of risk. The results also confirmed that individual differences in risk taking attitude
(Carver & White, 1994) and attitude towards renewable energy (Bang et al., 2003) impact
energy-related decision making under the risky choice framing and goal framing respectively, in
contrast to what prior research had suggested (Popov, 2012).
The basic analysis tested not only the direct relationship between framing and tariff selection
but also the contribution of the attitude towards renewable energy and risk taking attitude.
Results show that the positive attribute framing indeed has a significant impact on consumers’
energy tariff selection regardless of the attitude towards renewable energy and risk taking
attitude. The main effects of risky choice and goal framing on tariff selection did not yield
significant results, but after controlling for the attitude towards renewable energy and risk
taking attitude these results were altered. The impact of risky choice framing on tariff selection
yielded significant results for people with high risk taking attitude. Also, a slight significant
difference between the negative and positive goal framing was found for people with a less
positive attitude towards renewable energy when the analysis was based on the median split.
6.1.3 Discussing the Meta-analysis
The paper went beyond theory testing and focused on theory building based on the results
from the conducted meta-analysis. The paper researched the impact of framing on the
perceived energy tariff proximity as well as on the individual preference for energy tariff
attributes of risk and greenness. Finally, the findings of this paper had a strong internal validity
where the findings from one analysis were checked with the findings from the other analyses
that were part of the meta-analysis. Moreover, the tariff selection, our dependent variable, was
61
measured in multiple ways to ensure that measurement and scale reliability were in place (Hair
et al., 2010a).
In terms of individual preferences for tariff attributes, the positive risky choice frame, the
positive attribute frame and positive goal frame significantly influenced the people to choose
for riskier tariffs. The positive attribute frame had the strongest impact. On the other hand, the
positive and negative risky choice frames significantly influenced the people to choose for grey
instead of green tariffs. None of the frames however encouraged people to choose more often
for green tariffs, and in general people put more emphasis on the riskiness of tariffs than on
their greenness.
In terms of tariff proximity, the attribute and goal frames, both positive and negative, were
effective in bringing the grey flat tariff closer to the green real time pricing tariff with regards to
preference scores of the individuals. However, the risky choice frames were not effective in
bringing the traditional tariff closer to the more modern tariff. The negative attribute frame is
most efficient in increasing (post-treatment versus pre-treatment) the proximity between the
grey flat tariff and green real time pricing tariff.
6.2 Managerial Implications
The findings about the attribute framing are relevant from a scientific and practical point of
view. The positive attribute framing seems to be a particularly powerful and promising tool for
influencing energy consumers. Our research provides evidence suggesting that presenting the
information about green real time pricing tariff – arguably the most risky type of energy tariff
in the market today (Gottwalt et al., 2011; Weerdt et al., 2011) – in a positive way can indeed
make people more favorable towards it, independently of their attitude towards sustainable
energy and independent of their risk taking attitude. Energy companies like Eneco need to
explain the green real time pricing tariff to the consumers with regards to the percentage of
people that considered its use a success. Moreover, the energy companies should also provide
consumers with the percentage success rate.
62
The positive risky choice framing also appears to be a potentially good promotional tool on
people with high risk taking attitude. Thus, in case the energy providers choose the positive
risky choice framing as a promotion tool, they would need to promote it to people that are
usually risk taking by showing them the green real time pricing tariff in relation to other types
of tariffs and providing them with probabilities of saving money on energy bills in case they
switch to those tariffs. The real time pricing tariff however should be presented as the highest
saver on energy bills. Moreover, the positive goal framing also appears to be a promising tool
when used on people with a relatively less positive attitude towards renewable energy. This is
particularly important to know, since people with a less positive attitude towards green energy,
who seem more difficult to convince, can be convinced to choose the green real time pricing
tariff through the use of the positive goal framing. In this case, these people should be shown
the positive consequences (e.g. “provide with tools to pay less money per year”) of choosing
this modern type of pricing.
Next to knowing that the positive risky choice frame, positive attribute frame and positive goal
frame can be used to influence people to put more emphasis on risk, the energy providers
should also be cautious in using the positive risky choice frame, since it will influence people to
choose more often for grey tariffs. It is important that energy providers are aware of this
tradeoff when they choose to use the positive risky choice framing. Moreover, they should also
acknowledge that none of the frames were effective in making people choose more often for
green tariffs. Neither before nor after all the frames were applied, did people put much
emphasis on the degree of greenness, although they did tend to choose green tariffs more
often than grey tariffs. However, this was not a result of any of the frames, but rather the status
quo. Thus, the results of the paper should not be used for the purpose of convincing people to
choose for green tariffs but rather for convincing people to choose for riskier tariffs such as the
green real time pricing tariff.
The present research verified and confirmed that framing effects exist in the area of energy
tariff selection. Energy providers are advised on the basis of the findings of this paper to choose
to advertise their dynamic tariffs with the help of the positive attribute framing by providing
63
consumers with the success rate of the dynamic pricing (e.g. “70% of the people that tried
green real time pricing mentioned that choosing this tariff was a success and that they paid less
money per year by choosing this tariff”). The positive attribute framing proved to have the
highest impact on convincing people to choose for the dynamic pricing, achieved bringing
closer, in the perception of people, the traditional grey flat tariff and the modern green real
time pricing tariff and did not come with a trade-off as the positive risky choice framing did.
6.3 Limitations
One of the limitations of the study is that the conducted quasi-experiment did not control for
the environment. Due to this shortcoming we tried to increase the internal validity by
controlling for many possible disturbing factors, as well as increase the scale and measurement
reliability, by using several proven techniques (Hair et al., 2010a) and to increase external
validity by choosing for not controlled real settings. In future research the experiment could be
done in a pure laboratory setting to see if similar results come forward. Also a field experiment
could be done in order to see whether people would still choose the same tariffs in reality. This
quasi-experiment was conducted because testing the frames in reality might have impacted the
energy company in a negative way, in case the framing would have had undesirable effects.
However, since the positive attribute frame yielded great results, a field experiment can use the
positive attribute framing once dynamic pricing is in place.
Another limitation of the research is that the negative risky choice framing of the second
experiment had to be redone, since it was not correctly manipulated the first time. The
negative risky choice case was redone approximately three weeks later, during which time
people might have changed their opinion. On top of that, the respondents from the corrected
negative risky choice case were gathered only from one place of the university, namely the
cafeteria, while for all the other cases, the respondents were gathered from multiple settings
such as the cafeteria, classroom, TU Delft University, Erasmus University, train station, etc. In
further research, the respondents should be gathered only from one place at one time for all
the cases of the experiment.
64
Finally, the respondents in this research were students, who usually do not make decisions
concerning their energy provider. The question is whether decision making of consumers with a
large household will differ from that of students, who rarely have a large household. Because of
this, future studies should attempt to replicate the present results in a real consumer sample,
based on different groups of people from different locations, in order to increase the external
validity of the present findings. Finally, the small sample size of 50 respondents for each of the
six experiment cases may have been the cause for some of the insignificant results found.
Therefore, present findings should be replicated in a larger sample.
6.4 Further Research
Next to the further research that is derived from the limitations of this study, further research
should be done on the impact of goal framing on energy tariff selection. The impact on tariff
selection of the positive goal framing compared to the negative frame was slightly significant,
based on the median split, for people with a less positive attitude towards renewable energy.
However, after conducting the same analysis based on the attitude towards renewable energy
as a covariate, the results altered the significance of the impact. This gives only a weak
confirmation of the hypothesis that the attitude towards renewable energy has an impact on
the relationship between goal framing and tariff selection, and therefore should be further
researched in other studies.
6.5 Conclusion
All in all, this research sends an essential message and encourages energy providers to use the
positive attribute framing in their advertising strategy for the new dynamic prices. They should
also be aware that people are more interested in the degree of risk of a tariff than in its degree
of greenness. Moreover, the positive frames from the risky choice, attribute and goal framings
significantly influenced the people to choose for riskier tariffs. This leads us to conclude that if
the energy industry is truly interested in the stimulation of sustainable energy consumption
with dynamic pricing, and unwavering to truly implement the transition from traditional grid to
the smart grid, framing is of utmost importance.
65
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73
Appendix 1: Example of Experiment
Energy Decision Making Experiment (1)
Dear participant,
Thank you for taking time to participate in this experiment!
This survey investigates consumer decision-making in the energy domain. This issue is particularly
relevant given the current discussion on traditional versus renewable energy sources. Therefore, the aim
of the present study is to explore the extent to which individual preferences influence someone's
consumption of energy.
The survey takes about 20-25 minutes to fill out. Keep in mind that this study is concerned with your
own opinion. There are no right or wrong answers to the questions. Please, do NOT skip any question.
Your Student Number:
THE EXPERIMENT STARTS HERE:
________________________
1. You have $1,000 and you must pick one of the following choices: *
___
Choice A: You have a 50% chance of gaining $1,000, and a 50% chance of gaining $0.
Choice B: You have a 100% chance of gaining $500.
2. You have $2,000 and you must pick one of the following choices: *
Choice A: You have a 50% chance of losing $1,000, and 50% of losing $0.
Choice B: You have a 100% chance of losing $500.
74
Please use the scales below to indicate to what extent you disagree or agree with those statements. They concern your own opinion. Some questions have a Dutch
translation attached. It is a standardized Dutch translation that paraphrases the questions in English and does not literally translates them.
Very true
Somewhat true
Somewhat
Very false
for me
false for me
for me
for me
1. A person's family is the most important thing in life.
(DUTCH: Familie is het belangrijkste in iemands leven) *
1
2
3
4
2. Even if something bad is about to happen to me, I rarely experience fear or
nervousness.
(DUTCH: Ik voel zelden angst of zenuwen, zelfs als me iets vervelends staat te wachten) *
1
2
3
4
3. I go out of my way to get things I want.
(DUTCH: Ik zal over mijn grenzen heen gaan om de dingen te krijgen die ik wil) *
1
2
3
4
4. When I'm doing well at something I love to keep at it.
(DUTCH: Als ik iets goed doe, wil ik er graag mee doorgaan) *
1
2
3
4
5. I'm always willing to try something new if I think it will be fun.
(DUTCH: ik ben altijd bereid iets nieuws te proberen als ik denk dat het leuk zal zijn) *
1
2
3
4
6. How I dress is important to me.
(DUTCH: Kleren zijn belangrijk voor me) *
1
2
3
4
7. When I get something I want, I feel excited and energized.
(DUTCH: Als ik krijg wat ik wil, voel ik me me opgewonden en energiek) *
1
2
3
4
8. Criticism or scolding hurts me quite a bit.
(DUTCH: Kritiek of uitbranders raken mij behoorlijk) *
1
2
3
4
9. When I want something I usually go all-out to get it.
(DUTCH: Als ik iets wil, zal ik er gewoonlijk alles aan doen om dit te krijgen) *
1
2
3
4
10. I will often do things for no other reason than that they might be fun.
(DUTCH: Vaak doe ik dingen alleen voor de lol) *
1
2
3
4
11. It's hard for me to find the time to do things such as get a haircut.
(DUTCH: Ik heb vaak weinig tijd om dingen te doen) *
1
2
3
4
12. If I see a chance to get something I want I move on it right away.
(DUTCH: Als ik de kans zie iets te krijgen wat ik wil, zal ik die kans meteen grijpen) *
1
2
3
4
75
Very true
for me
Somewhat true
for me
Somewhat
false for me
Very false
for me
13. I feel pretty worried or upset when I think or know somebody is angry at me.
(DUTCH: Ik voel me bezorgd of overstuur als ik denk of weet dat iemand boos op mij is) *
1
2
3
4
14. When I see an opportunity for something I like I get excited right away.
(DUTCH: Als ik ergens een buitenkansje zie dan word ik meteen enthousiast) *
1
2
3
4
15. I often act on the spur of the moment.
(DUTCH: Ik doe vaak dingen in een vlaag van opwelling) *
1
2
3
4
16. If I think something unpleasant is going to happen I usually get pretty "worked up."
(DUTCH: Ik raak enigszins gestrest als ik denk dat er iets vervelends staat te gebeuren) *
1
2
3
4
17. I often wonder why people act the way they do.
(DUTCH: Ik vraag me vaak af waarom mensen doen zoals ze doen) *
1
2
3
4
18. When good things happen to me, it affects me strongly.
(DUTCH: Als ik iets leuks meemaak heeft dat duidelijk invloed op me) *
1
2
3
4
19. I feel worried when I think I have done poorly at something important.
(DUTCH: Ik voel me bezorgd als ik denk dat ik slecht heb gepresteerd ) *
1
2
3
4
20. I crave excitement and new sensations.
(DUTCH: Ik verlang naar spanning en sensatie) *
1
2
3
4
21. When I go after something I use a "no holds barred" approach.
(DUTCH: Als ik iets van plan ben dan laat ik mij door niets weerhouden) *
1
2
3
4
22. I have very few fears compared to my friends.
(DUTCH: Ik ervaar weinig angsten vergeleken met mijn vrienden) *
1
2
3
4
23. It would excite me to win a contest.
(DUTCH: Als ik een wedstrijd zou winnen, zou ik erg enthousiast zijn) *
1
2
3
4
24. I worry about making mistakes.
(DUTCH:Ik pieker wel eens over het maken van fouten) *
1
2
3
4
76
TYPES OF ELECTRICITY TARIFFS
This part of the survey explains the difference between six types of electricity tariffs which are soon to be offered by energy suppliers. Currently, you are most likely
to pay a flat tariff - the same price for each unit (kWh) of consumed energy. However, the price of electricity on the wholesale markets differs substantially
throughout the day. Prices are higher during peak demand when most people return from work, and significantly lower during off-peak - late evening and night. New
tariff plans like Time of Use and Real Time Pricing give you the opportunity to save on your bill by adjusting the times when you run the appliances.
Keep in mind that there are many household appliances which consume a lot of energy (e.g. washing machine, dishwasher, iron, water heating boiler) that can be
postponed to off-peak hours when prices are low.
Flat tariff - the price per kWh consumed energy is the same
during the day. No matter what time you run the appliances, you
pay the same price (below you can see an example of a flat tariff)
Time of Use tariff - the 24 hours of a day are divided into 3 time
periods: on-peak when the prices are high, mid-peak, and offpeak when the prices are low. The graph below shows these three
zones and the corresponding prices (below you can see an
example of a time of use tariff). PLEASE NOTE THAT these
different hourly prices are every day the same.
Real time pricing tariff - the price per kWh differs every hour
throughout the day according to the wholesale market conditions
and the actual network load. The price pattern generally
resembles the one of the Time of Use tariff - there are morning
and evening peaks with high prices. During the rest of the day, the
prices are comparatively lower which allows for savings on the
bill. PLEASE NOTE THAT every day the hourly prices are different.
You get to know one day in advance the prices for each hour of
each day so that you know when is the lowest price to start your
appliances such as your washing machine(below you can see an
example of a real time pricing tariff).
77
Traditional vs. Renewable energy: Any of the three tariffs (flat, time of use and real time pricing) can be either Grey (with 9% renewable energy in the total energy
mix) or a Green one (with 100% renewable energy in the total energy mix). Renewable energy comes from wind, sun or water and is being used due to the increase
of fossil fuel prices and the environmental problems caused by the use of conventional fuels.
There are three types of tariffs (flat, time of use and real time pricing) and two attributes (green, grey) that each type of tariff can have. In total there are six
combinations: flat grey tariff, flat green tariff, time of use grey tariff, time of use green tariff, real time pricing grey tariff and real time pricing green tariff: You can
see below the six types of tariffs:
THE TARIFFS A
CONSUMER
NEEDS TO
CHOOSE FROM
(A
COMBINATION
OF 3 TYPES OF
TARIFF AND 2
ATTRIBUTES:
GREEN OR GREY
Short explanation:
 Flat tariff – same price every hour of the day, every day
 Time of Use tariff – different prices during the day, but they are the same every day
 Real Time Pricing tariff – different prices during the day, they change every day according to the market conditions
 Green – 100% renewable energy
 Grey – 9% renewable energy
78
PLEASE ANSWER THE FOLLOWING QUESTIONS CAREFULLY. THANK YOU! :)
25. Please specify how likely it is to choose each of the tariffs by RANKING them in the table below. You can
order the tariffs by entering a numeric value for the priority. A lower value represents a higher rank for
the tariff!
In this way give the number 1 to the most preferred tariff, the number 2 to the second most preferred
tariff, etc. and the number 6 to the least preferred tariff.
Grey flat tariff
Green flat tariff
Grey time of use tariff
Green time of use tariff
Grey real time pricing tariff
Green real time pricing tariff
26. Please specify again how likely it is to choose each of the tariffs. This time please give a numeric value
from 1 to 100 to each of the following tariffs, that most adequately describes your preference.
A higher preference will be expressed by means of a higher number!!
Grey flat tariff (put a number between 1 and 100)
Green flat tariff (put a number between 1 and 100)
Grey time of use tariff (put a number between 1 and 100)
Green time of use tariff (put a number between 1 and 100)
Grey real time pricing tariff (put a number between 1 and 100)
Green real time pricing tariff (put a number between 1 and 100)
79
ADDITIONAL INFORMATION
Imagine that you already have an energy tariff. Now the time comes for you to choose another tariff. Please
assume that your consumption remains the same also in the coming years. The following tariffs are in
comparison to your old tariff that you already have.
If you choose grey flat tariff, you will pay 300 euro per year less.
If you choose green flat tariff, there is a 90% probability that you will pay 333 euro and 33 cent less per year
and a 10% probability that you will pay the same.
If you choose grey time of use tariff, there is an 80% probability that you will pay 375 euro less per year and a
20% probability that you will pay the same.
If you choose green time of use tariff, there is a 70% probability that you will pay 428 euro and 57 cent less
per year and a 30% probability that you will pay the same.
If you choose grey real time pricing tariff, there is a 60% probability that you will pay 500 euro less per year
and a 40% probability that you will pay the same.
If you choose green real time pricing tariff, there is a 50% probability that you will pay 600 euro less.
27. Which of the tariffs would you favor now? *
Grey flat tariff
Green flat tariff
Grey time of use tariff
Green time of use tariff
Grey real time pricing tariff
Green real time pricing tariff
28. How confident are you with the choice you made? *
Completely sure
Mostly sure
Somewhat sure
Neither sure nor unsure
Somewhat unsure
Mostly unsure
Completely unsure
80
PLEASE ANSWER THE FOLLOWING QUESTIONS CAREFULLY. THEY ARE THE SAME QUESTIONS YOU WERE
ASKED PREVIOUSLY BUT THIS TIME PLEASE USE THE ADDITIONAL INFORMATION GIVEN. AGAIN, PLEASE
THINK ABOUT THE ADDITIONAL INFORMATION YOU JUST READ (PAGE 7). THANK YOU! :)
29. Please specify how likely it is to choose each of the tariffs by RANKING them in the table below. You can
order the tariffs by entering a numeric value for the priority. A lower value represents a higher rank for
the tariff!
In this way give the number 1 to the most preferred tariff, the number 2 to the second most preferred tariff,
etc. and the number 6 to the least preferred tariff.
Grey flat tariff
Green flat tariff
Grey time of use tariff
Green time of use tariff
Grey real time pricing tariff
Green real time pricing tariff
30. Please specify again how likely it is to choose each of the tariffs. This time please give a numeric value
from 1 to 100 to each of the following tariffs, that most adequately describes your preference.
A higher preference will be expressed by means of a higher number!!
Grey flat tariff (put a number between 1 and 100)
Green flat tariff (put a number between 1 and 100)
Grey time of use tariff (put a number between 1 and 100)
Green time of use tariff (put a number between 1 and 100)
Grey real time pricing tariff (put a number between 1 and 100)
Green real time pricing tariff (put a number between 1 and 100)
81
POST QUESTIONS
Please use the scales below to indicate to what extent you disagree or agree with those statements. They concern your own opinion and there are no right or wrong
answers. Please answer ALL questions - do NOT leave a statement unanswered!
Not at all true
for me
Very true
for me
31. Using renewable energy does not make any difference for me. *
1
2
3
4
5
6
7
32. Whether the energy used in my household is renewable is of no concern to me. *
1
2
3
4
5
6
7
33. Using renewable energy is not worth the price I would have to pay. *
1
2
3
4
5
6
7
34. The fact that my household uses renewable energy would make me feel better about
myself. *
1
2
3
4
5
6
7
1
2
3
4
5
6
7
36. Concern about using renewable energy influences my decisions about the energy
consumption. *
1
2
3
4
5
6
7
37. I find this study stupid. *
1
2
3
4
5
6
7
38. I find the entire study boring. *
1
2
3
4
5
6
7
35. The possibility of renewable energy being used in my household means a lot to me.
*
39. Did you take breaks while working on this study? *
Yes
No
40. If yes, how many?
82
41. What is your age? *
42. What is your gender? *
Female
Male
43. What is the highest level of education you have completed? *
High School
Bachelor's Degree
Master's Degree
PhD or above
44. Do you have any comments about the experiment?
Thank you very much for your participation! :)
83
Appendix 2: CAPCA Output
Table 18: CATPCA Output
Pretest
Posttest
Pretest
Posttest
Pretest
Posttest
Pretest
Posttest
All Frames
Goal Framing
Attribute Framing
Risky Choice Framing
Positive
Dimens
ion
Cronbach'
s Alpha
1
Negative
Cronbach'
s Alpha
.746
Total
(Eigenval
ue)b
2.642
2
.219
Total
Total
Cronbach'
s Alpha
.616
*Total
(Eigenval
ue) b
2.053
.685
* Total
(Eigenval
ue) b
2.329
1.223
.397
1.494
.294
1.324
.890a
3.865
.862a
3.547
.871a
3.653
1
.910
4.145
.680
2.308
.714
2.468
2
-.016
.986
.540
1.818
.442
1.583
Total
.966a
5.132
.909a
4.126
.904a
4.051
1
.706
2.431
.471
1.646
.598
1.993
2
.421
1.540
.451
1.602
.402
1.504
Total
.898a
3.971
.831a
3.248
.857a
3.497
1
.764
2.753
.574
1.917
.672
2.274
2
.267
1.287
.432
1.563
.395
1.490
Total
.903a
4.040
.855a
3.480
.881a
3.764
1
.679
2.302
.646
2.168
.632
2.112
2
.466
1.635
.498
1.709
.516
1.755
Total
.895a
3.937
.890a
3.877
.890a
3.867
1
.698
2.390
.776
2.827
.732
2.561
2
.397
1.495
.601
2.002
.493
1.697
Total
.891a
3.886
.951a
4.829
.918a
4.258
1
.690
2.354
.540
1.817
.766
2.765
2
.377
1,459
.467
1.637
.375
1.455
Total
.885a
3.812
.853a
3.454
.916a
4.220
1
.721
2.506
.653
2.196
.670
2.265
2
.372
1.448
.503
1.723
.456
1.613
Total
.897a
3.954
.894a
3.918
.891a
3.878
a Total Cronbach's Alpha is based on the total Eigenvalue.
b Variance Accounted for – Total Eigenvalue
84
Appendix 3: Vector Angles
Table 19: Tariff Proximity
Angle between the Grey Flat and Green RTP vectors
all frames
pretest 85,8 degrees
posttest
Risky Choice
Framing
positive
negative
Attribute
Framing
positive
negative
Goal
Framing
positive
negative
109,2 degrees
pretest
88,6
degrees
posttest
93,5
degrees
pretest
posttest
94,4
degrees
posttest
89,4
degrees
Conclusion about
the proximity of the
two tariffs
increased
decreased
4,8
degrees
increased
decreased
19,3
degrees
increased
decreased
-5,0
degrees
decreased
increased
-29,5
degrees
decreased
increased
-20,0
degrees
decreased
increased
-6,2
degrees
decreased
increased
118,0 degrees
88,5 degrees
pretest
110,1 degrees
posttest
90,1
pretest
posttest
Conclusion
about the
angle
86,6 degrees
106,0 degrees
pretest
pretest
posttest
Difference in
angles between
pretest and
posttest
23,4
degrees
degrees
109,6 degrees
103,4 degrees
85
Appendix 4: List of Tables
Table 1 – Summary of Methodological Differences in Risky Choice, Attribute and Goal Framing
Table 2 – Hypothesis
Table 3 – Cases of the Experiment
Table 4 – Content of Frames and Measurement of the Tariff Selection
Table 5 – Measurement of the Tariff Selection
Table 6 – Attributes Values
Table 7 – Descriptive Results
Table 8 – Preferred Tariff under Risky Choice Framing
Table 9 – Green Real Time Pricing under Attribute Framing
Table 10 – Univariate Analysis of Variance: Summary of Experiment 2
Table 11 – Green Real Time Pricing under Goal Framing
Table 12 – Univariate Analysis of Variance: Summary of Experiment 3
Table 13 – Part Worth Utilities and Relative Weights
Table 14 – CATPCA Output for All Frames Together
Table 15 – CATPCA Output for the Risky Choice Framing
Table 16 – CATPCA Output for the Attribute Framing
Table 17 – CATPCA Output for the Goal Framing
Table 18 – CATPCA Output
Table 19 – Tariff Proximity
86
Appendix 5: List of Figures
Figure 1 – Eneco Source of Electricity
Figure 2 – Hedging Cost Premiums
Figure 3 – The Six Types of Energy Tariffs
Figure 4 – Risky Choice Framing
Figure 5 – Attribute Framing
Figure 6 – Goal Framing
Figure 7 – Conceptual Model
87
88