=Paper=
{{Paper
|id=Vol-2629/1_poster_armayones.pdf
|storemode=property
|title=Relationship between Behavior Fogg Model and Behavior Change Technique Taxonomy 93: Implications for Behavior Designers
|pdfUrl=https://ceur-ws.org/Vol-2629/1_poster_armayones.pdf
|volume=Vol-2629
|authors=Manuel Armayones,Benigna Gómez-Zúñiga,Noemí Robles,Modesta Pousada,Eulàlia Hernàndez
|dblpUrl=https://dblp.org/rec/conf/persuasive/ArmayonesGRPH20
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==Relationship between Behavior Fogg Model and Behavior Change Technique Taxonomy 93: Implications for Behavior Designers==
https://ceur-ws.org/Vol-2629/1_poster_armayones.pdf
Relationship between Behavior Fogg Model and Behavior
Change Technique Taxonomy 93 (V1): implications for
behavior designers
Manuel Armayones1, Benigna Gómez-Zúñiga2, Noemí Robles1 Modesta Pousada2
Eulàlia Hernàndez,1,2
1 eHealth Center Open University of Catalonia
2 Psychology and Educational Studies. Open University of Catalonia
marmayones@uoc.edu
Abstract. BCT taxonomy v1 is an extensive taxonomy of 93 consensually
agreed, distinct Behavior Change Technics (BCTs). The BCT taxonomy offers
a step change as a method for specifying interventions and becomes a very use-
ful way to systematize a set of 93 BCT that have been developed thorough a
Delphy study by Michie.
The Behavior Fogg Model (BFM) explains in an elegant and easy way how
behavior happens when three elements converge at the same moment: Motiva-
tion, Ability, and Prompt.
Thus, BCT inventory is the most exhaustive and well-validated set of behavior
change techniques that any researcher/professional can use both to analyze psy-
chological interventions, with and without technology use. On the other hand
BFM allows researchers and practitioners to design intervention in which the
behavioral change techniques have a key role and allow research-
ers/professionals to analyze the "persuasibility" of an intervention. In this
framework, our research question were: Could we find a relationship between
BFM and BCT Inventory? Could we classify the 93 BCT's in the three "dimen-
sions" of BFM model?
Methods: Two researchers classified the 93 BCT’s V1 in three categories
following an ad-hoc criterion: D1 Motivation, D2: Ability and D3: Prompt.
Results showed that it is possible to categorize the 93 BCT’s techniques in
the dimensions of Ability, Motivation and Prompt of BFM. Analyzing the 93
BCT’s we concluded that it exists more techniques related to “Motivation” and
“Prompt” Dimensions, that with “Ability” Dimension.
Conclusion We need further research to analyze the implication in psycho-
logical intervention aimed to change behaviors. As first conclusion we consid-
ered that since Fogg considers that the Ability dimension increases the adoption
of a specific behavior it would be useful to make a reflection about how to in-
crease the number of BCT’s related with Ability dimension in our interventions.
Keywords: Behavior Fogg Model, BCT Taxonomy, psychology, theoretical
analysis, behavior change,
Keywords: First Keyword, Second Keyword, Third Keyword.
Persuasive 2020, Adjunct proceedings of the 15th International conference on Persuasive
Technology. Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0)
1 Background
1.1 BCT Taxonomy V1 93 & Behavior Fogg Model (B=MAP)
BCT taxonomy v1 is an extensive taxonomy of 93 consensually agreed, distinct
BCTs(1) developed by Michie and colleagues thorough a Delphi study. The BCT
taxonomy offers a step change as a method for specifying interventions and becomes
a very useful way to systematize a set of 93 Behavioral Change Techniques (BCTs).
The BCT v1 has been used in fields such physical activity(2), musculoskeletal pain
(3), patient engagement(4), fall prevention programs (5) or diet habits (6). On the
other hand Behavior Fogg Model (BFM) (7) explains in an elegant and easy way how
behavior happens when three elements converge at the same moment: Motivation,
Ability, and Prompt. In a recent work Fogg explained that modifying the Ability of
subjects, making easier to do a specific behavior (both increasing subjects’ skills or
starting with a tiny behavior) increased the likelihood that this behavior happens if the
motivator and prompt appear at the same time (8)
Apparently these two approaches to behavior design, BCT Taxonomy which ana-
lyzes the “active ingredient” of the interventions and becomes a tool for analysis and
reflection about how to design using very specific techniques; and the BFM, that is a
model to explain how behaviors work, does not seem to have a direct relationship and
for some researchers and behavior designers can be some confusing to figure out how
they can be looked in an integrative view.
Our research questions were: Could we find relationship between BFM and BCT
Inventory? Could we classify the 93 BCT's in the three "dimensions" of BFM model?
2 Methods
The analysis was carried out by two independent researchers (MA and BG) who clas-
sified the 93 BCT in according the Fogg Model dimensions: D1: Motivation, D2:
Ability, D3:Prompt.
In case of controversy, a third researcher (MP) took the final decision of assignment.
Before the first round of assignment a set of specific criteria that we outlined were
agreed:
D1: Motivation: When the BCT referred to psychological aspects of the
person such as their attitudes, their social interaction with other people or
the emotional outcome of their behavior.
D2: Ability: When BCT referred to the subject's difficulties in carrying
out a behavior and the actions to simplify this or improve their skills.
D3: Prompt: When BCT referred to some element of the person's physical
context.
Once classified the whole set of 93 BCT a second analysis to assign a “main di-
mension” (M, A or P) to the 16 categories of BCT were performed.
Persuasive 2020, Adjunct proceedings of the 15th International conference on Persuasive
Technology. Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0)
3
If more than the 70% of techniques of a specific BCT Cluster were classified in a
specific dimension we considered that this Cluster was mainly related with this spe-
cific dimension. In some cases, and after the third expert judgement, it was not possi-
ble to achieve an agreement and the BCT Cluster was tagged in more than one BFM
Dimension.
3 Results
Table 1 shows the main finding of the study.
BCT V1 93 Cluster D1: Motiva- D2:Ability D3 Main BFM Dimension
tion Prompt
1. Goals and planning 4 4 1 Motivation & Ability
2. Feedback and moni- 5 1 1 Motivation
toring
3. Social support 1 1 1 Motivation & Ability & Prompt
4. Shaping knowledge 1 2 1 Ability
5. Natural consequenc- 6 0 0 Motivation
es
6. Comparison of be- 2 1 0 Motivation
havior
7. Associations 0 1 7 Prompt
8. Repetition and sub- 0 3 4 Prompt
stitution
9. Comparison of out- 3 0 0 Motivation
comes
10. Reward and threat 11 0 0 Motivation
11. Regulation 1 3 0 Ability
12. Antecedents 0 1 5 Prompt
13. Identity 5 0 0 Motivation
14. Scheduled conse- 10 0 0 Motivation
quences
15. Self-belief 4 0 0 Motivation
16. Covert learning 3 0 0 Motivation
Total 56 17 20
4 Conclusions
Despite some overlapping’s between BFM Dimensions and BCT Clusters we could
see that mostly BCT’s were related with D1: Motivation. That has some implication
4
for behavior designers. On the one hand a big number of BCTs come from the classi-
cal theories of Psychology and have been adapted for online intervention sometimes
without considering that in presential practice psychologist can work very directly
patient’s motivation. On the other hand, authors like BJ Fogg explain that try to inter-
vene in motivation is very hard, saying literally: «Motivation is Unreliable»(8) if you
want to help people to create healthy habits».
Finally, bearing in mind these aspects, we would like to share some ideas for reflec-
tion: Firstly, if most of the BCT’s that we are using in our design are aiming to in-
crease motivation perhaps we should shift to more “Ability related” techniques. Sec-
ondly, applying a “simple” design in a e.g. Mobile app, will make it «easy for all» but
try to motivate the whole public is difficult and very expensive. Perhaps with IA this
aspect will change offering tailored BCTs for individuals. Finally, nowadays in the
field of BCTs we are very focused on motivate people and perhaps this is not the best
way to persuade when we use online interventions.
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