In recent years many times sustainability and renewable energy consumption have been set on the agenda. However, the pressing issue how to make people reduce their amount of energy consumed - or their switching towards green alternatives - has received far less research attention. The academic discipline of behavioral economics has much to offer to this debate. In the presentation we will summarize prior research on the role of individual differences and various pricing and framing techniques that have proven to be helpful in making people switch to green energy. We will also address challenges and future directions in behavioral energy economics.

1. Current Directions in Behavioral Energy
Economics
Laurens Rook
July 17, 2015
Alpen-Adria University Klagenfurt

2. Who am I?
 Assistant Professor at Delft University of Technology
(TPM)
 Lecturer Research Methods and Statistics / Group
Dynamics / Organizational Psychology
 PhD at Erasmus University Rotterdam -> individual and
small group research (2008)
 MA at University of Amsterdam -> mass psychology
(2001)

3. Research Interests
(1) Creative cognition research
(2) Behavioral economics: biases and heuristics in the
making of choices -> applied to future energy business
 My research methods: laboratory / online experiments
and surveys

4. Key Collaborators in Behavioral
Energy Economics research
 Sudip Bhattacharjee (University of Connecticut)
 Wolfgang Ketter (Rotterdam School of Management)
 Markus Zanker (Alpen-Adria University, Klagenfurt)

5. Outline for today
 Introduction into the problem of (renewable) energy
 Behavioral economics and future energy preferences
 Personality psychology and future energy preferences
 Directions for future research

6. Today’s energy landscape
6

7. Future Energy Business

8. Future Energy Tariffs and Their
Consequences
 Fixed tariffs =energy consumption relatively
insensitive to fluctuations in energy prices (energy
markets in most countries currently employ fixed
tariffs)
 Flexible tariffs = energy consumption is subject to
fluctuations in energy prices (i.e., renewable but
imbalanced energy)

9. Future Energy Tariffs and Their
Consequences
Hedging Cost Premiums (Faruqui & Wood, 2008)

10. Energy Tariffs and Their Behavioral
Consequences
 Fixed tariffs =energy consumption relatively
insensitive to fluctuations in energy prices (a safe
and certain situation)
 Flexible tariffs = energy consumption is subject to
fluctuations in energy prices (i.e., renewable but
imbalanced energy; a risky and uncertain situation)

11. Our Core Experimental Paradigm

12. Behavioral economics and today’s
energy landscape
12

13. Behavioral Economics:
Valenced framing: when people’s choices are influenced by the manner in
which options are presented

14. The Asian disease problem
Imagine that the United States is preparing for an outbreak of an unusual
Asian disease that is expected to kill 600 people. A number of alternative
programs to combat the disease have been proposed. Scientific estimates
of the consequences of the programs are:
Program A: If Program A is adopted, 200 people will be saved.
Program B: 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.
Program C: If Program C is adopted, 400 people will die.
Program D: 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, 1981

15. Major framing effects
 Risky choice framing = when people evaluate an object / event
based on its (positive-negative; risky-safe) characteristics
 Attribute framing = when people evaluate an object / event
based on its (positive-negative) characteristics
 Goal framing = when the goal (end-state) of an action or
behavior is (positively-negatively) framed
Levin et al., 1998

16. Framing effects (general
predictions)
 Risky choice framing = people are more willing to take risks [to
avoid a loss] under negative (vs. positive) risky choice frames
 Attribute framing = positive attribute frames are more effective
than negative attribute frames
 Goal framing = negative goal frames are more effective than
positive goal frames
NB – intrinsic self-relevance – Krishnamurthy et al., 2001

17. Our hypotheses
 H1 - Risky choice framing = people will prefer riskier energy
tariffs under a negative than under a positive frame
 H2 - Attribute framing = people will evaluate a RTP tariff better
under a positive than under a negative attribute frame
 H3 - Goal framing = people will prefer a RTP tariff under a
negative than under a positive goal frame

18. Energy Preferences: Individual
Differences?
very slightly extremely
or not at all
Using renewable energy does not make any
difference to me
1 2 3 4 5 6 7
Whether the energy used in my household is
renewable is of no concern to me
1 2 3 4 5 6 7
Using renewable energy is not worth the price I
would have to pay
1 2 3 4 5 6 7
The fact that my household uses renewable energy
would make me feel better of myself
1 2 3 4 5 6 7
The possibility of renewable energy being used in
my household means a lot to me
1 2 3 4 5 6 7
Concern about using renewable energy influences
my decisions about the energy consumption
1 2 3 4 5 6 7
Bang et al., 2000

19. Methodology
 Three (30 min pencil-and-paper) experiments with
similar procedure:
 Measuring campus students’ attitude toward renewable
energy
 Experimental treatment (a valenced frame)
 An energy tariff selection task

20. The Experimental Paradigm
NOTE – participants could for each three tariff types choose between
a grey and a green version, yielding six possibilities

21. Experiment 1
 One hundred and four students (71 men and 33
women, M age = 22.83, SD = 3.81)
 Random assignment to a (positive, negative) risky
choice frame
 Individual attitude toward renewable energy, age,
and gender added as covariates

22. Manipulation risky choice frame
 As in Kahneman and Tversky’s Asian disease
problem, but adapted to energy tariffs

23. Results

24. Results (II)
 Individual attitude toward renewable energy added
as covariate
 High: over-representation of green flat (under
negative frame), and green time of use & real time
tariffs (under positive frame)
 Low: mild preference for green flat (under negative
frame), and over-representation of all gray tariffs
(under positive frame)

25. Experiment 2
 Ninety nine students (63 men and 36 women, M age
= 22.82, SD = 4.40)
 Random assignment to a (positive, negative)
attribute frame
 Individual attitude toward renewable energy , age,
and gender added as covariates

26. Manipulation attribute frame
 As in Kahneman and Tversky’s paradigm, each
energy tariff was presented either in positive or
negative terms – depending on experimental
conditions

27. Results
 Positive attribute frame: M = 2.239, SD = 1.239
 Negative attribute frame: M = 4.163, SD = 1.632
 mean difference -1.822, ts = -5.930, p < .0001
 People prefer a positively attributed green real time
pricing tariff over a negatively framed one

28. Results (II)
 Individual attitude toward renewable energy, age,
and gender added as covariates
 Same pattern: people prefer a positively attributed
green real time pricing tariff over a negatively framed
one regardless of attitudinal preferences…

29. Experiment 3
 One hundred and seven students (60 men and 47
women, M age = 23.59, SD = 5.28)
 Random assignment to a (positive, negative) goal
frame
 Individual attitude toward renewable energy, age,
and gender added as covariates

30. Manipulation goal frame
 As in Kahneman and Tversky’s paradigm, each
energy tariff was presented either in positive or
negative terms – depending on experimental
conditions – and:
 modified such that it tapped into (either) a risk-
seeking or risk-avoidant end-state regarding energy
consumption terms – depending on experimental
conditions

31. Results
 Positive goal frame: M = 3.229, SD = 1.627
 Negative goal frame: M = 3.568, SD = 1.797
 mean difference -0.339, tp = -0.950, p < .345
 Goal framing did not significantly influence people’s
energy tariff selection

32. No effects for goal framing. Why?
 We did something wrong (i.e., a confounded design)
 There was something special to our sample
(analogous to the notion of intrinsic self-relevance )

33. Results (II)
 Individual attitude toward renewable energy:
 High: Positive goal frame: M = 3.095, SD = 1.671
Negative goal frame: M = 2.778, SD = 1.865
mean difference 0.317, tp = 0.560, p = 0.578
 Low: Positive goal frame: M = 3.333, SD = 1.617
Negative goal frame: M = 4.155, SD = 1.558
mean difference 0.782, tp = -1.790, p = 0.079

34. Conclusion
 Valenced-based framing does influence customer
energy tariff selection
 We can steer people’s choice toward choosing
“green” (when we apply risky choice or attribute -
but not goal – frames)
 We confirmed that individual attitude toward
renewable energy is important (but not necessary) to
establish that

35. Limitations
 Our Experiment 1 – large number of tariff attributes
without a proper control
 Our Experiments 2 & 3 – a single attribute of one
type of tariff (green RTP)
 Solution = We are currently running a simplified risky
choice framing study

36. Limitations (II)
 Our Experiment 3 (on goal framing) did not work,
because of a confounded design
[A] take action and get gain
[B] not take action and do not get gain
[C] take action and avoid loss
[D] not take action and incur loss
[Rothman & Salovey, 1997]

37. Limitations (III)
 Our Experiments 1-3 rely on a student sample
instead of real households involved in tariff selection
on an annual basis
 We are currently running the same study on
Amazon’s MechTurk among a more representational
sample

38. Personality psychology and today’s
energy landscape
38

39. Kurt Lewin’s law of interaction
B = f (P, E)
B = the behavior of the person
P = personal characteristics of the individual
E = environmental (task type) factors

40. Self-report measures for cognitive
neuroscience
40
Source: sachaepskamp.com

42. Biopsychological approaches to
personality
 Temperament and Character Inventory = a four-
factor neurobiological model and measurement scale
(Cloninger)
 The BIS/BAS Scales = a multifactor neurobiological
model that accounts for risk-seeking vs. risk-avoidant
tendencies (Carver & Schreier, 1994)
 The Big Five = a pragmatic five-factor model of
personality (Costa & McCrae, 1993, 1997)

43. Illustration: The TamagoCar project
 Researchers: Ksenia Koroleva and Wolf Ketter (RSM),
Laurens Rook
 The TamagoCar app investigates (1) how different prices for
battery charging influence efficient driving of an e-vehicle in
competition, and (2) under which circumstances people may
experience range anxiety
 Part of the project was a behavioral pre-survey with
self-reports on BIS/BAS, the Big Five, and energy-
related attitudes
43

44. Presented: Pre-analysis (correlations)
BIS/BAS
PANAS
IPIP / Five-
Factor
Attitude
toward
Renewable
Energy

45. The BIS/BAS Scales
 Three fundamental emotional processes exist in the human brain
(Gray, 1987, 1989):
1. Behavioral Inhibition System (BIS; avoidance behavior in
response to threats and novel stimuli)
2. Behavioral Activation System (BAS; approach behavior in
response to incentives)
3. Fight-Flight System (rapid responses to immediate threats)
 BIS and BAS explain goal-directed behavior beyond emergency
settings: how people may respond to rewards, stimuli
(information), and threats
45

46. The BIS/BAS scales
 Carver and White (1994) developed a self-report
measure for BIS and BAS, and is widely used in
cognitive neuroscience to complement fMRI and other
brain scanning studie.:
The BIS scale is 7 items, unidimensional
The BAS scale is 13 items, 3 sub-dimensions
4 items are fillers / distractors
46

47. The BIS scale and prediction
 Example item: “I worry about making mistakes”
 Someone high (vs. low) on BIS is generally more
nervous and may experience any sort of anxiety in
novel or threatening situations
47

48. The BAS Scale (I)
Example item (BAS Reward Responsiveness): “When I
get something I want, I feel excited and energized”
Example item (BAS Drive): “When I want something, I
usually go all-out to get it”
Example item (BAS Fun Seeking): “I’m always willing to
try something new if I think it will be fun”
48

49. The BAS Scale and prediction
 Someone high on BAS is generally more sensitive to
positive signals of rewards in novel or threatening
situations, and may experience less anxiety
49

50. The Positive Affect Negative Affect
Scale (PANAS)
 Watson, Clark and White (1988) developed the PANAS
scales to measure self-reported PA and NA:
The PA scale is 10 items, unidimensional
The NA scale is 10 items, unidimensional
 Consistent with the literature, we took the trait (“in
general”) version of the PANAS
50

51. The PANAS scales and predictions
 Negative Affect will correlate highly with overall BIS
sensitivity (cf., Gomez et al., 2002)
 Positive Affect will correlate highly with overall BAS
sensitivity (cf., Gomez et al., 2002)
51

52. Theory
 The Big Five or Five-Factor Model is the dominant
model of personality structure in personality
psychology (cf., Costa & McCrae, 1992) consisting of:
1. Extraversion; outgoing / energetic vs. solitary / reserved
2. Agreeableness;
3. Conscientiousness;
4. Neuroticism; sensitive / nervous vs. secure / confident
5. Openness;
52

53. Visual: The Big Five
Source: sachaepskamp.com

54. The Mini-IPIP scales
 The Big Five (Costa & McCrae, 1985) is very large (240
items)
 The Mini-IPIP was developed as a psychometrically
acceptable, short, measure of the Big Five factors of
personality (Donnellan, Oswald, Baird, & Lucas, 2006)
 4 measures per Big Five trait with comparable
convergent, discriminant and criterion-related validity
54

55. The Mini-IPIP scales and predictions
 The BIS is believed to underlie Neuroticism (cf., Watson et al.,
1999) and thus can be assumed to correlate with Neuroticism
 The BAS is believed to underlie Extraversion (cf., Watson et al.,
1999) and thus can be assumed to correlate with Extraversion
55

56. Three behavioral moderators
1. The BIS/BAS scales : people either approach or avoid action in
presence of novel stimuli and threats, and with affective
consequences (occurrence of general anxiety)
The BIS/BAS scales have two neighboring personality constructs:
2. The PANAS: PA correlates with BAS; NA correlates with BIS
3. The Mini-IPIP – Five-Factor Model: Neuroticism correlates with
BIS; Extraversion with BAS
56

57. In a conceptual model
57
BIS/BAS
Self-
reported
Range
Anxiety
PANAS
IPIP / Five-
Factor

58. The sample
 A total of 264 participated in the study
 Data of 57 participants were excluded due to missing
values
 The sample used in the analyses consisted of 207
students (142 men and 65 women; Mage = 22.87; SD
= 1.94)
58

59. Reliability and correlations
59

60. Summarizing
 The TamagoCar project illustrates how:
 You can use self-report measures from cognitive
neuroscience to predict and test individual differences
in human preferences
 Also in research on energy-related topics

61. Future Directions
 Cognitive Neuroscience: Biopsychological self-report
measures set the stage for fMRI-studies
 Behavioral Energy Informatics: When experimental
designs include (smart) devices (i.e., apps),
psychological methods can be linked to other analytical
tools
 from highly controlled to bigger, messier data
 higher external validity