Matching  costs  to  context:    Status  quo  bias,  temporal  framing,    and  household  energy  decisions  

Carrie  Gill1,  Stephen  Atlas2,  and  David  Hardisty3  1  PhD  Candidate,  Department  of  Environmental  and  Natural  Resource  Economics,  University  of  Rhode  Island

2  Assistant  Professor  of  MarkeCng,  College  of  Business  AdministraCon,  University  of  Rhode  Island   3  Assistant  Professor,  MarkeCng  and  Behavioral  Science  Division,  Sauder  School  of  Business,  University  of  BriCsh  Columbia

US  residenCal  consumers  used  10.2  quadrillion  BTU  of  energy  in  2009 •  Amounts  to  nearly  $230  billion  spent  on  uClity  bills •  NegaCve  environmental  and  health  externaliCes

MoCvaCon:  reduce  energy  consumpCon

Status  quo  bias  may  lead  to  inefficiency

Individuals  tend  to  sCck  with  defaults  and  status  quos

Individuals  tend  to  keep  contractor’s  arbitrary  light  bulb  choice

Dinner  et  al.  2011

Make  costs  salient  –  but  how?

This  cup  of  coffee  costs  $1.80.

This  cup  of  coffee  costs  180  pennies.

Framing  is  important

CogniCve  fluency  affects  decision  making

FLUENCY:  The  subjecCve  experience  of  ease  of  processing  informaCon Using  familiar,  easy  to  read,  compare,  understand  descripCons  leads  to… •  Product  a^racCveness   •  IntenCons  to  exercise  and  cook •  Choosing  fuel-­‐efficient  cars

Difficulty  in  construcCng  preferences  (e.g.  hard  to  disCnguish  between  a^ributes)  may  result  in  deferred  choices

Lembregts  and  Pandelaere  2013;  Song  and  Schwarz  2008;  Camilleri  and  Larrick  2014;  Novemsky  et  al  2007    

ObjecCves

•  IdenCfy  which  temporal  frame  of  costs/savings  reduces  status  quo  bias  in  energy  decisions  (Study  1)

•  InvesCgate  the  role  of  cogniCve  fluency  (Study  2)

•  Explore  the  effect  of  providing  context  for  evaluaCng  costs  in  less-­‐fluent  frames  (Study  3)

Online  survey:  N  =  353  MTurkers,  between-­‐subjects  experiment.   We  manipulated  status  quo  behavior  (inefficient,  efficient)  and  how  the  costs  or  savings  of  switching  behaviors  was  framed  (per  day,  per  month,  per  year).  

Study  1:  Status  quo  bias  and  cost  frame

Status  Quo  Behavior  

Inefficient

Efficient

Cost  Frame  

Per  Day Per  Month Per  Year

Significant  interacCon  effect F  (2,  347)  =  4.33;  p  <  0.05

***  and  **  indicate  significance  at  the  1%  and  5%  levels,  respecCvely,  using  the  per  family  error  rate.

Simple  main  effects  of  status  quo  at  temporal  frame:

Daily:  F  (1,  347)  =  17.27***

Monthly:  F  (1,  347)  =  0.01

Yearly:  F  (1,  347)  =  7.01**

When  doing  the  laundry,  you  can  choose  what  water  temperature  your  washing  machine  uses.

Some  people  choose  to  use  warm  or  hot  water  because  they  think  warmer  water  is  most  effec>ve  for  cleaning  laundry.

However,  warm  and  hot  water  use  more  energy  than  cold  water.

Suppose  you  oBen  use  warm  water,  but  you  are  considering  whether  to  use  only  cold  water  for  your  laundry.  

If  you  only  use  cold  water,  you  will  save  17  cents  per  day  on  energy  costs.   What  do  you  think  you  would  do?  Please  answer  on  the  following  scale  where  10  means  that  you  would  definitely  only  use  cold  water  and  0  means  you  would  definitely  use  warm  water.

Study  1:  Example  scenario  and  treatment

10 7 0 1 2 3

Definitely  warm  water

4 5 6 8 9

Definitely  cold  water

Significant  interacCon  effect F  (2,  347)  =  4.33;  p  <  0.05

***  and  **  indicate  significance  at  the  1%  and  5%  levels,  respecCvely,  using  the  per  family  error  rate.

Simple  main  effects  of  status  quo  at  temporal  frame:

Daily:  F  (1,  347)  =  17.27***

Monthly:  F  (1,  347)  =  0.01

Yearly:  F  (1,  347)  =  7.01**

When  doing  the  laundry,  you  can  choose  what  water  temperature  your  washing  machine  uses.

Some  people  choose  to  use  warm  or  hot  water  because  they  think  warmer  water  is  most  effec>ve  for  cleaning  laundry.

However,  warm  and  hot  water  use  more  energy  than  cold  water.

Suppose  you  oBen  use  warm  water,  but  you  are  considering  whether  to  use  only  cold  water  for  your  laundry.  

If  you  only  use  warm  water,  you  will  pay  17  cents  more  per  day  on  energy  costs.   What  do  you  think  you  would  do?  Please  answer  on  the  following  scale  where  10  means  that  you  would  definitely  only  use  cold  water  and  0  means  you  would  definitely  use  warm  water.

Study  1:  Example  scenario  and  treatment

10 7 0 1 2 3

Definitely  warm  water

4 5 6 8 9

Definitely  cold  water

Significant  interacCon  effect F  (2,  347)  =  4.33;  p  <  0.05

***  and  **  indicate  significance  at  the  1%  and  5%  levels,  respecCvely,  using  the  per  family  error  rate.

Simple  main  effects  of  status  quo  at  temporal  frame:

Daily:  F  (1,  347)  =  17.27***

Monthly:  F  (1,  347)  =  0.01

Yearly:  F  (1,  347)  =  7.01**

When  doing  the  laundry,  you  can  choose  what  water  temperature  your  washing  machine  uses.

Some  people  choose  to  use  warm  or  hot  water  because  they  think  warmer  water  is  most  effec>ve  for  cleaning  laundry.

However,  warm  and  hot  water  use  more  energy  than  cold  water.

Suppose  you  oBen  use  warm  water,  but  you  are  considering  whether  to  use  only  cold  water  for  your  laundry.  

If  you  only  use  warm  water,  you  will  pay  $5.25  more  per  month  on  energy  costs.   What  do  you  think  you  would  do?  Please  answer  on  the  following  scale  where  10  means  that  you  would  definitely  only  use  cold  water  and  0  means  you  would  definitely  use  warm  water.

Study  1:  Example  scenario  and  treatment

10 7 0 1 2 3

Definitely  warm  water

4 5 6 8 9

Definitely  cold  water

Significant  interacCon  effect F  (2,  347)  =  4.33;  p  <  0.05

***  and  **  indicate  significance  at  the  1%  and  5%  levels,  respecCvely,  using  the  per  family  error  rate.

Simple  main  effects  of  status  quo  at  temporal  frame:

Daily:  F  (1,  347)  =  17.27***

Monthly:  F  (1,  347)  =  0.01

Yearly:  F  (1,  347)  =  7.01**

When  doing  the  laundry,  you  can  choose  what  water  temperature  your  washing  machine  uses.

Some  people  choose  to  use  warm  or  hot  water  because  they  think  warmer  water  is  most  effec>ve  for  cleaning  laundry.

However,  warm  and  hot  water  use  more  energy  than  cold  water.

Suppose  you  oBen  use  warm  water,  but  you  are  considering  whether  to  use  only  cold  water  for  your  laundry.  

If  you  only  use  warm  water,  you  will  pay  $63  more  per  year  on  energy  costs.   What  do  you  think  you  would  do?  Please  answer  on  the  following  scale  where  10  means  that  you  would  definitely  only  use  cold  water  and  0  means  you  would  definitely  use  warm  water.

Study  1:  Example  scenario  and  treatment

10 7 0 1 2 3

Definitely  warm  water

4 5 6 8 9

Definitely  cold  water

Significant  interacCon  effect F  (2,  347)  =  4.33;  p  <  0.05

***  and  **  indicate  significance  at  the  1%  and  5%  levels,  respecCvely,  using  the  per  family  error  rate.

Simple  main  effects  of  status  quo  at  temporal  frame:

Daily:  F  (1,  347)  =  17.27***

Monthly:  F  (1,  347)  =  0.01

Yearly:  F  (1,  347)  =  7.01**

Many  homes  have  a  second  fridge  or  freezer.  They  are  typically  used  for  addi>onal  food  storage  or  convenience,  and  are  oBen  located  in  a  garage  or  basement.

Suppose  you  own  a  second  fridge  and  are  deciding  what  to  do  with  it?

If  it’s  plugged  in,  you  can  use  it  to  keep  things  cold,  but  must  pay  for  its  energy  costs.

If  it’s  not  plugged  in,  you  can  store  it  in  the  house,  give  it  away  or  sell  it.

Suppose  you  are  considering  whether  to  disconnect  a  second  fridge  in  your  house.

If  you  disconnect  the  second  fridge,  you  will  save  $10.00  per  month  on  energy  costs.   What  do  you  think  you  would  do?  Please  answer  on  the  following  scale  where  10  means  that  you  would  definitely  disconnect  the  fridge  and  0  means  you  would  definitely  connect  the  fridge.

Study  1:  Example  scenario  and  treatment

10 7 0 1 2 3

Definitely  connect

4 5 6 8 9

Definitely  disconnect

7.02

5.19

6.85 6.827.56

6.42

01

23

45

67

89

10B

ehav

ior I

nten

tion

[0: i

neffi

cien

t - 1

0: e

ffici

ent]

Daily Costs Monthly Costs Yearly Costs

Efficient Status Quo Inefficient Status Quo

N=353, +/- 1 standard error shown

Study  1:  Status  quo  bias  minimized  in  monthly  frame

Study  1:  ANOVA  and  regression  results

df [1] [2] [3] [4]Status Quo 1 F = 15.93*** F = 20.24***Efficient omitted omittedInefficient -1.827 (0.439)*** -1.820 (0.427)***

Cost Frame 2 F = 4.67*** F = 5.35***Per Day omitted omittedPer Month -0.166 (0.438) -0.172 (0.424)Per Year 0.541 (0.441) 0.672 (0.430)

Interaction 2 F = 4.33** F = 3.59**Ineff. x Month 1.793 (0.616)*** 1.581 (0.598)***Ineff. x Year 0.684 (0.616) 0.589 (0.599)

ControlsLoss Aversion Coef. 1 F = 1.90 0.0510 (0.0370)Construal Level 1 F = 1.09 -0.572 (0.548)Cognitivie Reflection 1 F = 1.52 0.531 (0.431)Environmental Concern 1 F = 24.77*** 0.729 (0.146)***Constant 7.018 (0.317)*** 6.981 (0.484)***Obs 353 353 353 353R-squared 0.090 0.159 0.09 0.159

Analysis of Variance OLS RegressionDV = Behavior Intention [0: inefficient - 10: efficient]

Notes: *, **, and *** represent statistical significance at probabilities < 0.1, 0.05, and 0.01, respectively. F statistics shown for analysis of variance (columns 1 and 2). Regression coefficients with standard errors in parentheses shown for regression analysis (columns 3 and 4).

7.02

5.19

6.85 6.827.56

6.42

01

23

45

67

89

10B

ehav

ior I

nten

tion

[0: i

neffi

cien

t - 1

0: e

ffici

ent]

Daily Costs Monthly Costs Yearly Costs

Efficient Status Quo Inefficient Status Quo

N=353, +/- 1 standard error shown

Study  1:  Status  quo  bias  minimized  in  monthly  frame

*** and ** indicate significance at the 1% and 5% levels, respectively, using the per family error rate.

Simple  Main  Effect  of  Status  Quo  at  Cost  Frame

*** not  sig. **

à  Monthly  framing  minimizes  status  quo  bias  

7.02

5.19

6.85 6.827.56

6.42

01

23

45

67

89

10B

ehav

ior I

nten

tion

[0: i

neffi

cien

t - 1

0: e

ffici

ent]

Daily Costs Monthly Costs Yearly Costs

Efficient Status Quo Inefficient Status Quo

N=353, +/- 1 standard error shown

Could  monthly  energy  bills  provide  an  implicit  context  to  help  evaluate  costs?

Study  1:  Status  quo  bias  minimized  in  monthly  frame

Study  2:  CogniCve  fluency  across  frames

Online  survey:  N  =  1,199  MTurkers,  between-­‐subjects  experiment.   Same  scenario  set-­‐up  as  Study  1.  We  elicited  fluency  using  five  measures  and  and  compared  across  cost  frames.

Fluency  measures  on  7-­‐point  Likert  scale:

•  Es>ma>ng  how  the  energy  decision  would  financially  impact  me  was… •  Understanding  what  the  energy  decision  meant  was… •  The  descrip>on  of  the  financial  impact  seemed… •  The  descrip>on  of  the  energy  decision  seemed… •  How  involved  were  you  in  the  energy  decision?

-0.1

0.0

0.1

Flue

ncy

Inde

x

Daily Costs Monthly Costs Yearly Costs

N=1,199 +/- 1 SE

Study  2:  Fluency  is  highest  in  the  monthly  frame

Cronbach’s  α  =  0.790

df [1] [2] [3] [4]Cost Frame 2 F = 3.48** F = 3.99**Per Day omitted omittedPer Month 0.120 (0.052)** 0.127 (0.051)**Per Year 0.002 (0.052) 0.003 (0.051)

ControlsCare about saving money 1 F = 17.08*** 0.118 (0.028)***Care about environment 1 F = 7.82*** 0.065 (0.023)***Constant -0.041 (0.037) -0.833 (0.138)***Obs 1,199 1,199 1,199 1,199R-squared 0.006 0.035 0.006 0.035

DV = Cognitive Fluency [larger = more fluent]Analysis of Variance OLS Regression

Notes: *, **, and *** represent statistical significance at probabilities < 0.1, 0.05, and 0.01, respectively. F statistics shown for analysis of variance (columns 1 and 2). Regression coefficients with standard errors in parentheses shown for regression analysis (columns 3 and 4).

Study  2:  Fluency  is  highest  in  the  monthly  frame

-0.1

0.0

0.1

Flue

ncy

Inde

x

Daily Costs Monthly Costs Yearly Costs

N=1,199 +/- 1 SE

à  Individuals  are  most  fluent  with  monthly  costs  

Study  2:  Fluency  is  highest  in  the  monthly  frame

Your energy spending over 1 day�

3.75 Energy'Meter'

$� per day�

ENERGY COMPANY

Study  3:  Provide  context  for  unfamiliar  frames

Online  survey:  N  =  132  Qualtrics  recruits,  between-­‐subjects  experiment We  provide  an  explicit  context  for  total  energy  spending  (ler  fig.)  and  costs  of  engaging  in  energy-­‐inefficient  behaviors  (right  fig.)

Daily  cost  of  inefficiency Daily  spending  context

Study  3:  Provide  context  for  unfamiliar  frames

Online  survey:  N  =  132  Qualtrics  recruits,  between-­‐subjects  experiment We  provide  an  explicit  context  for  total  energy  spending  (ler  fig.)  and  costs  of  engaging  in  energy-­‐inefficient  behaviors  (right  fig.)

Yearly  cost  of  inefficiency Yearly  spending  context

Your energy spending over 1 year�

1,370 Energy'Meter'

$� per year�

ENERGY COMPANY

7.70

6.73

7.588.01

01

23

45

67

89

10B

ehav

ior I

nten

tion:

[0: i

neffi

cien

t - 1

0: e

ffici

ent]

Daily Spending Yearly Spending

Daily Costs Yearly Costs

N=132, +/- 1 standard error shown

Study  3:  Provide  context  for  unfamiliar  frames

Study  3:  Provide  context  for  unfamiliar  frames

df [1] [2] [3] [4]Per Year Cost Frame 1 F = 0.82 F = 2.38 -0.124 (0.423) -1.22 (0.420)***Per Year Spending Frame 1 F = 3.70* F = 2.61 -0.971 (0.427)** -0.294 (0.412)Interaction 1 F = 5.47** F = 6.97*** 1.400 (0.599)** 1.533 (0.581)***

ControlsConstrual Level 1 F = 0.29 -0.062 (0.115)Math Ability 1 F = 0.00 0.009 (0.953)Environmental Concern 1 F = 9.15*** -0.575 (0.190)***Care about Saving Money 1 F = 1.26 0.293 (0.261)Constant 7.705 (0.290)*** 6.024 (1.870)***Obs 132 132 132 132R-squared 0.070 0.160 0.070 0.160

DV = Behavior Intention [0: inefficient - 10: efficient]Analysis of Variance OLS Regression

Notes: *, **, and *** represent statistical significance at probabilities < 0.1, 0.05, and 0.01, respectively. F statistics shown for analysis of variance (columns 1 and 2). Regression coefficients with standard errors in parentheses shown for regression analysis (columns 3 and 4).

7.70

6.73

7.588.01

01

23

45

67

89

10B

ehav

ior I

nten

tion:

[0: i

neffi

cien

t - 1

0: e

ffici

ent]

Daily Spending Yearly Spending

Daily Costs Yearly Costs

N=132, +/- 1 standard error shown

Study  3:  Provide  context  for  unfamiliar  frames

Simple  Main  Effect  of  Cost  Frame  at  Spending  Frame

** not  sig.

** indicates significance at the 5% level using the per family error rate.

7.70

6.73

7.588.01

01

23

45

67

89

10B

ehav

ior I

nten

tion:

[0: i

neffi

cien

t - 1

0: e

ffici

ent]

Daily Spending Yearly Spending

Daily Costs Yearly Costs

N=132, +/- 1 standard error shown

à  Supple-­‐menIng  cost  info  with  a  spending  context  in  the  same  frame  can  increase  efficiency  intenIons  

Study  3:  Provide  context  for  unfamiliar  frames

When  targeCng  an  energy-­‐inefficient  audience  without  an  explicit  spending  context  –  consider  framing  costs  as  per  month.   Providing  a  context  for  total  energy  spending  in  the  same  frame  as  costs  of  energy  inefficiency  may  help  reduce  resistance  to  adopCng  energy-­‐efficient  behaviors.   Yearly  framing  of  costs/savings  coupled  with  annual  total  energy  spending  may  encourage  the  most  energy  efficient  behavior  intenCons.  Future  research  will  invesCgate  when  aggregaCng  costs  broadly  is  most  effecCve. Future  research  will  apply  findings  to  improve  Energy  Guide  labels  and  translate  intenCons  of  energy  efficiency  to  electricity  and  monetary  savings.  

Conclusions

Thanks  to  Ethan  Rix,  URI’s  Undergraduate  Research  IniCaCve  Grant,  Qualtrics  Behavioral  Research  Grant,  URI’s  Mental  AccounCng  and  Pricing  Lab,  ENRE  Experimental  Economics  Group,  and  to  a^endees  of  the  2015  Annual  MeeCng  of  the  Society  for  Judgment  and  Decision  Making.

Acknowledgements

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